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MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens | Jiemin Fang, Lingxi Xie, Xinggang Wang, Xiaopeng Zhang, Wenyu Liu, Qi Tian | Transformers have offered a new methodology of designing neural networks for visual recognition. Compared to convolutional networks, Transformers enjoy the ability of referring to global features at each stage, yet the attention module brings higher computational overhead that obstructs the application of Transformers to process high-resolution visual data. This paper aims to alleviate the conflict between efficiency and flexibility, for which we propose a specialized token for each region that serves as a messenger (MSG). Hence, by manipulating these MSG tokens, one can flexibly exchange visual information across regions and the computational complexity is reduced. We then integrate the MSG token into a multi-scale architecture named MSG-Transformer. In standard image classification and object detection, MSG-Transformer achieves competitive performance and the inference on both GPU and CPU is accelerated. Code is available at https://github.com/hustvl/MSG-Transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_MSG-Transformer_Exchanging_Local_Spatial_Information_by_Manipulating_Messenger_Tokens_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fang_MSG-Transformer_Exchanging_Local_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_MSG-Transformer_Exchanging_Local_Spatial_Information_by_Manipulating_Messenger_Tokens_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_MSG-Transformer_Exchanging_Local_Spatial_Information_by_Manipulating_Messenger_Tokens_CVPR_2022_paper.html | CVPR 2022 | null |
Cross Modal Retrieval With Querybank Normalisation | Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin, Yang Liu, Samuel Albanie | Profiting from large-scale training datasets, advances in neural architecture design and efficient inference, joint embeddings have become the dominant approach for tackling cross-modal retrieval. In this work we first show that, despite their effectiveness, state-of-the-art joint embeddings suffer significantly from the longstanding "hubness problem" in which a small number of gallery embeddings form the nearest neighbours of many queries. Drawing inspiration from the NLP literature, we formulate a simple but effective framework called Querybank Normalisation (QB-Norm) that re-normalises query similarities to account for hubs in the embedding space. QB-Norm improves retrieval performance without requiring retraining. Differently from prior work, we show that QB-Norm works effectively without concurrent access to any test set queries. Within the QB-Norm framework, we also propose a novel similarity normalisation method, the Dynamic Inverted Softmax, that is significantly more robust than existing approaches. We showcase QB-Norm across a range of cross modal retrieval models and benchmarks where it consistently enhances strong baselines beyond the state of the art. Code is available at https://vladbogo.github.io/QB-Norm/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bogolin_Cross_Modal_Retrieval_With_Querybank_Normalisation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bogolin_Cross_Modal_Retrieval_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.12777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bogolin_Cross_Modal_Retrieval_With_Querybank_Normalisation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bogolin_Cross_Modal_Retrieval_With_Querybank_Normalisation_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning | Jian Meng, Li Yang, Jinwoo Shin, Deliang Fan, Jae-sun Seo | Contrastive learning (or its variants) has recently become a promising direction in the self-supervised learning domain, achieving similar performance as supervised learning with minimum fine-tuning. Despite the labeling efficiency, wide and large networks are required to achieve high accuracy, which incurs a high amount of computation and hinders the pragmatic merit of self-supervised learning. To effectively reduce the computation of insignificant features or channels, recent dynamic pruning algorithms for supervised learning employed auxiliary salience predictors. However, we found that such salience predictors cannot be easily trained when they are naively applied to contrastive learning from scratch. To address this issue, we propose contrastive dual gating(CDG), a novel dynamic pruning algorithm that skips the uninformative features during contrastive learning without hurting the trainability of the networks. We demonstrate the superiority of CDG with ResNet models for CIFAR-10, CIFAR-100, and ImageNet-100 datasets. Compared to our implementations of state-of-the-art dynamic pruning algorithms for self-supervised learning, CDG achieves up to 15% accuracy improvement for CIFAR-10 dataset with higher computation reduction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Meng_Contrastive_Dual_Gating_Learning_Sparse_Features_With_Contrastive_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_Contrastive_Dual_Gating_Learning_Sparse_Features_With_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_Contrastive_Dual_Gating_Learning_Sparse_Features_With_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Universal Photometric Stereo Network Using Global Lighting Contexts | Satoshi Ikehata | This paper tackles a new photometric stereo task, named universal photometric stereo. Unlike existing tasks that assumed specific physical lighting models; hence, drastically limited their usability, a solution algorithm of this task is supposed to work for objects with diverse shapes and materials under arbitrary lighting variations without assuming any specific models. To solve this extremely challenging task, we present a purely data-driven method, which eliminates the prior assumption of lighting by replacing the recovery of physical lighting parameters with the extraction of the generic lighting representation, named global lighting contexts. We use them like lighting parameters in a calibrated photometric stereo network to recover surface normal vectors pixelwisely. To adapt our network to a wide variety of shapes, materials and lightings, it is trained on a new synthetic dataset which simulates the appearance of objects in the wild. Our method is compared with other state-of-the-art uncalibrated photometric stereo methods on our test data to demonstrate the significance of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ikehata_Universal_Photometric_Stereo_Network_Using_Global_Lighting_Contexts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ikehata_Universal_Photometric_Stereo_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ikehata_Universal_Photometric_Stereo_Network_Using_Global_Lighting_Contexts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ikehata_Universal_Photometric_Stereo_Network_Using_Global_Lighting_Contexts_CVPR_2022_paper.html | CVPR 2022 | null |
Hire-MLP: Vision MLP via Hierarchical Rearrangement | Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang | Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information. Such approach withholds MLPs from getting comparable performance with their transformer-based counterparts and prevents them from becoming a general backbone for computer vision. This paper presents Hire-MLP, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements. Specifically, the inner-region rearrangement is proposed to capture local information inside a spatial region, and the cross-region rearrangement is proposed to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions. Extensive experiments demonstrate the effectiveness of Hire-MLP as a versatile backbone for various vision tasks. In particular, Hire-MLP achieves competitive results on image classification, object detection and semantic segmentation tasks, e.g., 83.8% top-1 accuracy on ImageNet, 51.7% box AP and 44.8% mask AP on COCO val2017, and 49.9% mIoU on ADE20K, surpassing previous transformer-based and MLP-based models with better trade-off for accuracy and throughput. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Hire-MLP_Vision_MLP_via_Hierarchical_Rearrangement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Hire-MLP_Vision_MLP_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Hire-MLP_Vision_MLP_via_Hierarchical_Rearrangement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Hire-MLP_Vision_MLP_via_Hierarchical_Rearrangement_CVPR_2022_paper.html | CVPR 2022 | null |
Ray3D: Ray-Based 3D Human Pose Estimation for Monocular Absolute 3D Localization | Yu Zhan, Fenghai Li, Renliang Weng, Wongun Choi | In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhan_Ray3D_Ray-Based_3D_Human_Pose_Estimation_for_Monocular_Absolute_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhan_Ray3D_Ray-Based_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11471 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Ray3D_Ray-Based_3D_Human_Pose_Estimation_for_Monocular_Absolute_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Ray3D_Ray-Based_3D_Human_Pose_Estimation_for_Monocular_Absolute_3D_CVPR_2022_paper.html | CVPR 2022 | null |
Occluded Human Mesh Recovery | Rawal Khirodkar, Shashank Tripathi, Kris Kitani | Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task. Unfortunately, the single-human assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down methods have difficulties in recovering accurate 3D human meshes under severe person-person occlusion. To address this, we present Occluded Human Mesh Recovery (OCHMR) - a novel top-down mesh recovery approach that incorporates image spatial context to overcome the limitations of the single-human assumption. The approach is conceptually simple and can be applied to any existing top-down architecture. Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps. To reason from the predicted body centermaps, we introduce Contextual Normalization (CoNorm) blocks to adaptively modulate intermediate features of the top-down model. The contextual conditioning helps our model disambiguate between two severely overlapping human bounding-boxes, making it robust to multi-person occlusion. Compared with state-of-the-art methods, OCHMR achieves superior performance on challenging multi-person benchmarks like 3DPW, CrowdPose, and OCHuman. Specifically, our proposed contextual reasoning architecture applied to the SPIN model with ResNet-50 backbone results in 75.2 PMPJPE on 3DPW-PC, 23.6 AP on CrowdPose, and 37.7 AP on OCHuman datasets, a significant improvement of 6.9 mm, 6.4 AP, and 20.8 AP respectively over the baseline. Code and models will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Khirodkar_Occluded_Human_Mesh_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13349 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Object Tracking Meets Moving UAV | Shuai Liu, Xin Li, Huchuan Lu, You He | Multi-object tracking in unmanned aerial vehicle (UAV) videos is an important vision task and can be applied in a wide range of applications. However, conventional multi-object trackers do not work well on UAV videos due to the challenging factors of irregular motion caused by moving camera and view change in 3D directions. In this paper, we propose a UAVMOT network specially for multi-object tracking in UAV views. The UAVMOT introduces an ID feature update module to enhance the object's feature association. To better handle the complex motions under UAV views, we develop an adaptive motion filter module. In addition, a gradient balanced focal loss is used to tackle the imbalance categories and small objects detection problem. Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that the proposed UAVMOT achieves considerable improvement against the state-of-the-art tracking methods on UAV videos. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.html | CVPR 2022 | null |
ASM-Loc: Action-Aware Segment Modeling for Weakly-Supervised Temporal Action Localization | Bo He, Xitong Yang, Le Kang, Zhiyu Cheng, Xin Zhou, Abhinav Shrivastava | Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i.e., video snippets) are supervised by classifying labeled bags (i.e., untrimmed videos). However, this formulation typically treats snippets in a video as independent instances, ignoring the underlying temporal structures within and across action segments. To address this problem, we propose \system, a novel WTAL framework that enables explicit, action-aware segment modeling beyond standard MIL-based methods. Our framework entails three segment-centric components: (i) dynamic segment sampling for compensating the contribution of short actions; (ii) intra- and inter-segment attention for modeling action dynamics and capturing temporal dependencies; (iii) pseudo instance-level supervision for improving action boundary prediction. Furthermore, a multi-step refinement strategy is proposed to progressively improve action proposals along the model training process. Extensive experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of our approach, establishing new state of the art on both datasets. The code and models are publicly available at https://github.com/boheumd/ASM-Loc. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_ASM-Loc_Action-Aware_Segment_Modeling_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_ASM-Loc_Action-Aware_Segment_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_ASM-Loc_Action-Aware_Segment_Modeling_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_ASM-Loc_Action-Aware_Segment_Modeling_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition | Hongji Guo, Hanjing Wang, Qiang Ji | A complex action consists of a sequence of atomic actions that interact with each other over a relatively long period of time. This paper introduces a probabilistic model named Uncertainty-Guided Probabilistic Transformer (UGPT) for complex action recognition. The self-attention mechanism of a Transformer is used to capture the complex and long-term dynamics of the complex actions. By explicitly modeling the distribution of the attention scores, we extend the deterministic Transformer to a probabilistic Transformer in order to quantify the uncertainty of the prediction. The model prediction uncertainty is used to improve both training and inference. Specifically, we propose a novel training strategy by introducing a majority model and a minority model based on the epistemic uncertainty. During the inference, the prediction is jointly made by both models through a dynamic fusion strategy. Our method is validated on the benchmark datasets, including Breakfast Actions, MultiTHUMOS, and Charades. The experiment results show that our model achieves the state-of-the-art performance under both sufficient and insufficient data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Uncertainty-Guided_Probabilistic_Transformer_for_Complex_Action_Recognition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Uncertainty-Guided_Probabilistic_Transformer_for_Complex_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Uncertainty-Guided_Probabilistic_Transformer_for_Complex_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs | Xiaohan Ding, Xiangyu Zhang, Jungong Han, Guiguang Ding | We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depth-wise convolutions, to design efficient high-performance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31x31, in contrast to commonly used 3x3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias. Code & models at https://github.com/megvii-research/RepLKNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Scaling_Up_Your_Kernels_to_31x31_Revisiting_Large_Kernel_Design_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Scaling_Up_Your_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06717 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Scaling_Up_Your_Kernels_to_31x31_Revisiting_Large_Kernel_Design_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Scaling_Up_Your_Kernels_to_31x31_Revisiting_Large_Kernel_Design_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Multi-Person Pose Estimation With Transformers | Dahu Shi, Xing Wei, Liangqi Li, Ye Ren, Wenming Tan | Current methods of multi-person pose estimation typically treat the localization and association of body joints separately. In this paper, we propose the first fully end-to-end multi-person Pose Estimation framework with TRansformers, termed PETR. Our method views pose estimation as a hierarchical set prediction problem and effectively removes the need for many hand-crafted modules like RoI cropping, NMS and grouping post-processing. In PETR, multiple pose queries are learned to directly reason a set of full-body poses. Then a joint decoder is utilized to further refine the poses by exploring the kinematic relations between body joints. With the attention mechanism, the proposed method is able to adaptively attend to the features most relevant to target keypoints, which largely overcomes the feature misalignment difficulty in pose estimation and improves the performance considerably. Extensive experiments on the MS COCO and CrowdPose benchmarks show that PETR plays favorably against state-of-the-art approaches in terms of both accuracy and efficiency. The code and models are available at https://github.com/hikvision-research/opera. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_End-to-End_Multi-Person_Pose_Estimation_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
REGTR: End-to-End Point Cloud Correspondences With Transformers | Zi Jian Yew, Gim Hee Lee | Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of correspondences for pose estimation. In this work, we conjecture that attention mechanisms can replace the role of explicit feature matching and RANSAC, and thus propose an end-to-end framework to directly predict the final set of correspondences. We use a network architecture consisting primarily of transformer layers containing self and cross attentions, and train it to predict the probability each point lies in the overlapping region and its corresponding position in the other point cloud. The required rigid transformation can then be estimated directly from the predicted correspondences without further post-processing. Despite its simplicity, our approach achieves state-of-the-art performance on 3DMatch and ModelNet benchmarks. Our source code can be found at https://github.com/yewzijian/RegTR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yew_REGTR_End-to-End_Point_Cloud_Correspondences_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yew_REGTR_End-to-End_Point_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14517 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yew_REGTR_End-to-End_Point_Cloud_Correspondences_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yew_REGTR_End-to-End_Point_Cloud_Correspondences_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Neural 3D Scene Reconstruction With the Manhattan-World Assumption | Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, Xiaowei Zhou | This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality. The code and supplementary materials are available at https://zju3dv.github.io/manhattan_sdf. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Neural_3D_Scene_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.html | CVPR 2022 | null |
V2C: Visual Voice Cloning | Qi Chen, Mingkui Tan, Yuankai Qi, Jiaqiu Zhou, Yuanqing Li, Qi Wu | Existing Voice Cloning (VC) tasks aim to convert a paragraph text to a speech with desired voice specified by a reference audio. This has significantly boosted the development of artificial speech applications. However, there also exist many scenarios that cannot be well reflected by these VC tasks, such as movie dubbing, which requires the speech to be with emotions consistent with the movie plots. To fill this gap, in this work we propose a new task named Visual Voice Cloning (V2C), which seeks to convert a paragraph of text to a speech with both desired voice specified by a reference audio and desired emotion specified by a reference video. To facilitate research in this field, we construct a dataset, V2C-Animation, and propose a strong baseline based on existing state-of-the-art (SoTA) VC techniques. Our dataset contains 10,217 animated movie clips covering a large variety of genres (e.g., Comedy, Fantasy) and emotions (e.g., happy, sad). We further design a set of evaluation metrics, named MCD-DTW-SL, which help evaluate the similarity between ground-truth speeches and the synthesised ones. Extensive experimental results show that even SoTA VC methods cannot generate satisfying speeches for our V2C task. We hope the proposed new task together with the constructed dataset and evaluation metric will facilitate the research in the field of voice cloning and broader vision-and-language community. Source code and dataset will be released in https://github.com/chenqi008/V2C. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_V2C_Visual_Voice_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_V2C_Visual_Voice_Cloning_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection | Dongli Xu, Jinhong Deng, Wen Li | Average precision (AP) loss has recently shown promising performance on the dense object detection task. However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP) loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss. The first of these is a novel Adaptive Pairwise Error (APE) loss that focusing on ranking pairs in both positive and negative samples. Moreover, we select more accurate ranking pairs by exploiting the normalized ranking scores and localization scores with a clustering algorithm. Experiments conducted on the MS-COCO dataset support our analysis and demonstrate the superiority of our proposed method compared with current classification and ranking loss. The code is available at https://github.com/Xudangliatiger/APE-Loss. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Revisiting_AP_Loss_for_Dense_Object_Detection_Adaptive_Ranking_Pair_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Revisiting_AP_Loss_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Revisiting_AP_Loss_for_Dense_Object_Detection_Adaptive_Ranking_Pair_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Revisiting_AP_Loss_for_Dense_Object_Detection_Adaptive_Ranking_Pair_CVPR_2022_paper.html | CVPR 2022 | null |
3DeformRS: Certifying Spatial Deformations on Point Clouds | Gabriel Pérez S., Juan C. Pérez, Motasem Alfarra, Silvio Giancola, Bernard Ghanem | 3D computer vision models are commonly used in security-critical applications such as autonomous driving and surgical robotics. Emerging concerns over the robustness of these models against real-world deformations must be addressed practically and reliably. In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations. We developed 3DeformRS by building upon recent work that generalized Randomized Smoothing (RS) from pixel-intensity perturbations to vector-field deformations. In particular, we specialized RS to certify DNNs against parameterized deformations (e.g. rotation, twisting), while enjoying practical computational costs. We leverage the virtues of 3DeformRS to conduct a comprehensive empirical study on the certified robustness of four representative point cloud DNNs on two datasets and against seven different deformations. Compared to previous approaches for certifying point cloud DNNs, 3DeformRS is fast, scales well with point cloud size, and provides comparable-to-better certificates. For instance, when certifying a plain PointNet against a 3deg z-rotation on 1024-point clouds, 3DeformRS grants a certificate 3x larger and 20x faster than previous work. | https://openaccess.thecvf.com/content/CVPR2022/papers/S._3DeformRS_Certifying_Spatial_Deformations_on_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/S._3DeformRS_Certifying_Spatial_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/S._3DeformRS_Certifying_Spatial_Deformations_on_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/S._3DeformRS_Certifying_Spatial_Deformations_on_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses | Bastian Wandt, James J. Little, Helge Rhodin | Human pose estimation from single images is a challenging problem that is typically solved by supervised learning. Unfortunately, labeled training data does not yet exist for many human activities since 3D annotation requires dedicated motion capture systems. Therefore, we propose an unsupervised approach that learns to predict a 3D human pose from a single image while only being trained with 2D pose data, which can be crowd-sourced and is already widely available. To this end, we estimate the 3D pose that is most likely over random projections, with the likelihood estimated using normalizing flows on 2D poses. While previous work requires strong priors on camera rotations in the training data set, we learn the distribution of camera angles which significantly improves the performance. Another part of our contribution is to stabilize training with normalizing flows on high-dimensional 3D pose data by first projecting the 2D poses to a linear subspace. We outperform state-of-the-art in unsupervised human pose estimation on the benchmark dataset Human3.6M in all metrics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wandt_ElePose_Unsupervised_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.07088 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wandt_ElePose_Unsupervised_3D_Human_Pose_Estimation_by_Predicting_Camera_Elevation_CVPR_2022_paper.html | CVPR 2022 | null |
MAD: A Scalable Dataset for Language Grounding in Videos From Movie Audio Descriptions | Mattia Soldan, Alejandro Pardo, Juan León Alcázar, Fabian Caba, Chen Zhao, Silvio Giancola, Bernard Ghanem | The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these datasets for the video-language grounding task. Recent works have begun to discover significant limitations in these datasets, suggesting that state-of-the-art techniques commonly overfit to hidden dataset biases. In this work, we present MAD (Movie Audio Descriptions), a novel benchmark that departs from the paradigm of augmenting existing video datasets with text annotations and focuses on crawling and aligning available audio descriptions of mainstream movies. MAD contains over 384,000 natural language sentences grounded in over 1,200 hours of videos and exhibits a significant reduction in the currently diagnosed biases for video-language grounding datasets. MAD's collection strategy enables a novel and more challenging version of video-language grounding, where short temporal moments (typically seconds long) must be accurately grounded in diverse long-form videos that can last up to three hours. We have released MAD's data and baselines code at https://github.com/Soldelli/MAD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Soldan_MAD_A_Scalable_Dataset_for_Language_Grounding_in_Videos_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Soldan_MAD_A_Scalable_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Soldan_MAD_A_Scalable_Dataset_for_Language_Grounding_in_Videos_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Soldan_MAD_A_Scalable_Dataset_for_Language_Grounding_in_Videos_From_CVPR_2022_paper.html | CVPR 2022 | null |
EvUnroll: Neuromorphic Events Based Rolling Shutter Image Correction | Xinyu Zhou, Peiqi Duan, Yi Ma, Boxin Shi | This paper proposes to use neuromorphic events for correcting rolling shutter (RS) images as consecutive global shutter (GS) frames. RS effect introduces edge distortion and region occlusion into images caused by row-wise readout of CMOS sensors. We introduce a novel computational imaging setup consisting of an RS sensor and an event sensor, and propose a neural network called EvUnroll to solve this problem by exploring the high-temporal-resolution property of events. We use events to bridge a spatio-temporal connection between RS and GS, establish a flow estimation module to correct edge distortions, and design a synthesis-based restoration module to restore occluded regions. The results of two branches are fused through a refining module to generate corrected GS images. We further propose datasets captured by a high-speed camera and an RS-Event hybrid camera system for training and testing our network. Experimental results on both public and proposed datasets show a systematic performance improvement compared to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_EvUnroll_Neuromorphic_Events_Based_Rolling_Shutter_Image_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_EvUnroll_Neuromorphic_Events_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_EvUnroll_Neuromorphic_Events_Based_Rolling_Shutter_Image_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_EvUnroll_Neuromorphic_Events_Based_Rolling_Shutter_Image_Correction_CVPR_2022_paper.html | CVPR 2022 | null |
Gait Recognition in the Wild With Dense 3D Representations and a Benchmark | Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei | Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem. In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representation-based gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild. The code and dataset are available at: https://gait3d.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Gait_Recognition_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02569 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.html | CVPR 2022 | null |
ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis | Lixin Yang, Kailin Li, Xinyu Zhan, Jun Lv, Wenqiang Xu, Jiefeng Li, Cewu Lu | Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those diversities separately. However, constructing both valid and diverse hand-object interactions and efficiently learning from the vast synthetic data is still challenging. To address the above issues, we propose ArtiBoost, a lightweight online data enhancement method. ArtiBoost can cover diverse hand-object poses and camera viewpoints through sampling in a Composited hand-object Configuration and Viewpoint space (CCV-space) and can adaptively enrich the current hard-discernable items by loss-feedback and sample re-weighting. ArtiBoost alternatively performs data exploration and synthesis within a learning pipeline, and those synthetic data are blended into real-world source data for training. We apply ArtiBoost on a simple learning baseline network and witness the performance boost on several hand-object benchmarks. Our models and code are available at https://github.com/lixiny/ArtiBoost. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_ArtiBoost_Boosting_Articulated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.05488 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.html | CVPR 2022 | null |
Temporal Context Matters: Enhancing Single Image Prediction With Disease Progression Representations | Aishik Konwer, Xuan Xu, Joseph Bae, Chao Chen, Prateek Prasanna | Clinical outcome or severity prediction from medical images has largely focused on learning representations from single-timepoint or snapshot scans. It has been shown that disease progression can be better characterized by temporal imaging. We therefore hypothesized that outcome predictions can be improved by utilizing the disease progression information from sequential images. We present a deep learning approach that leverages temporal progression information to improve clinical outcome predictions from single-timepoint images. In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory. Meanwhile, a Vision Transformer is pretrained in a self-supervised fashion to extract features from single-timepoint images. The key contribution is to design a recalibration module that employs maximum mean discrepancy loss (MMD) to align distributions of the above two contextual representations. We train our system to predict clinical outcomes and severity grades from single-timepoint images. Experiments on chest and osteoarthritis radiography datasets demonstrate that our approach outperforms other state-of-the-art techniques. | https://openaccess.thecvf.com/content/CVPR2022/papers/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Konwer_Temporal_Context_Matters_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01933 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.html | CVPR 2022 | null |
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection | Chenhongyi Yang, Zehao Huang, Naiyan Wang | While general object detection with deep learning has achieved great success in the past few years, the performance and efficiency of detecting small objects are far from satisfactory. The most common and effective way to promote small object detection is to use high-resolution images or feature maps. However, both approaches induce costly computation since the computational cost grows squarely as the size of images and features increases. To get the best of two worlds, we propose QueryDet that uses a novel query mechanism to accelerate the inference speed of feature-pyramid based object detectors. The pipeline composes two steps: it first predicts the coarse locations of small objects on low-resolution features and then computes the accurate detection results using high-resolution features sparsely guided by those coarse positions. In this way, we can not only harvest the benefit of high-resolution feature maps but also avoid useless computation for the background area. On the popular COCO dataset, the proposed method improves the detection mAP by 1.0 and mAP small by 2.0, and the high-resolution inference speed is improved to 3.0x on average. On VisDrone dataset, which contains more small objects, we create a new state-of-the-art while gaining a 2.3x high-resolution acceleration on average. Code is available at https://github.com/ ChenhongyiYang/QueryDet-PyTorch. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2103.09136 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment | Yiming Zeng, Yue Qian, Qijian Zhang, Junhui Hou, Yixuan Yuan, Ying He | This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) and further reason that temporal irregularity and under-sampling are two major challenges. To tackle the challenges, we propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. Specifically, we propose a temporal consistency learning module to align two consecutive point cloud frames point-wisely, based on which we can employ linear interpolation to obtain coarse trajectories/in-between frames. To compensate the high-order nonlinear components of trajectories, we apply aligned feature embeddings that encode local geometry properties to regress point-wise increments, which are combined with the coarse estimations. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually. Our framework can bring benefits to 3D motion data acquisition. The source code is publicly available at https://github.com/ZENGYIMING-EAMON/IDEA-Net.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_IDEA-Net_Dynamic_3D_Point_Cloud_Interpolation_via_Deep_Embedding_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zeng_IDEA-Net_Dynamic_3D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_IDEA-Net_Dynamic_3D_Point_Cloud_Interpolation_via_Deep_Embedding_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_IDEA-Net_Dynamic_3D_Point_Cloud_Interpolation_via_Deep_Embedding_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning | Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard, Ajmal Mian, Mubarak Shah | Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data. Next, an off-the-shelf semi-supervised learning method is used for training where rejected samples are treated as unlabeled data. Our comprehensive analysis shows that current selection methods disproportionately select samples from easy (fast learnable) classes while rejecting those from relatively harder ones. This creates class imbalance in the selected clean set and in turn, deteriorates performance under high label noise. In this work, we propose UNICON, a simple yet effective sample selection method which is robust to high label noise. To address the disproportionate selection of easy and hard samples, we introduce a Jensen-Shannon divergence based uniform selection mechanism which does not require any probabilistic modeling and hyperparameter tuning. We complement our selection method with contrastive learning to further combat the memorization of noisy labels. Extensive experimentation on multiple benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate. Our code is publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Karim_UniCon_Combating_Label_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14542 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Karim_UniCon_Combating_Label_Noise_Through_Uniform_Selection_and_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Learning From All Vehicles | Dian Chen, Philipp Krähenbühl | In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Learning_From_All_Vehicles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Learning_From_All_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Learning_From_All_Vehicles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Learning_From_All_Vehicles_CVPR_2022_paper.html | CVPR 2022 | null |
BEHAVE: Dataset and Method for Tracking Human Object Interactions | Bharat Lal Bhatnagar, Xianghui Xie, Ilya A. Petrov, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll | Modelling interactions between humans and objects in natural environments is central to many applications including gaming, virtual and mixed reality, as well as human behavior analysis and human-robot collaboration. This challenging operation scenario requires generalization to vast number of objects, scenes, and human actions. Unfortunately, there exist no such dataset. Moreover, this data needs to be acquired in diverse natural environments, which rules out 4D scanners and marker based capture systems. We present BEHAVE dataset, the first full body human-object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We record 15k frames at 5 locations with 8 subjects performing a wide range of interactions with 20 common objects. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup. Our key insight is to predict correspondences from the human and the object to a statistical body model to obtain human-object contacts during interactions. Our approach can record and track not just the humans and objects but also their interactions, modeled as surface contacts, in 3D. Our code and data can be found at: http://virtualhumans.mpi-inf.mpg.de/behave. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhatnagar_BEHAVE_Dataset_and_Method_for_Tracking_Human_Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhatnagar_BEHAVE_Dataset_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06950 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bhatnagar_BEHAVE_Dataset_and_Method_for_Tracking_Human_Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bhatnagar_BEHAVE_Dataset_and_Method_for_Tracking_Human_Object_Interactions_CVPR_2022_paper.html | CVPR 2022 | null |
Disentangled3D: Learning a 3D Generative Model With Disentangled Geometry and Appearance From Monocular Images | Ayush Tewari, Mallikarjun B R, Xingang Pan, Ohad Fried, Maneesh Agrawala, Christian Theobalt | Learning 3D generative models from a dataset of monocular images enables self-supervised 3D reasoning and controllable synthesis. State-of-the-art 3D generative models are GANs which use neural 3D volumetric representations for synthesis. Images are synthesized by rendering the volumes from a given camera. These models can disentangle the 3D scene from the camera viewpoint in any generated image. However, most models do not disentangle other factors of image formation, such as geometry and appearance. In this paper, we design a 3D GAN which can learn a disentangled model of objects, just from monocular observations. Our model can disentangle the geometry and appearance variations in the scene, i.e., we can independently sample from the geometry and appearance spaces of the generative model. This is achieved using a novel non-rigid deformable scene formulation. A 3D volume which represents an object instance is computed as a non-rigidly deformed canonical 3D volume. Our method learns the canonical volume, as well as its deformations, jointly during training. This formulation also helps us improve the disentanglement between the 3D scene and the camera viewpoints using a novel pose regularization loss defined on the 3D deformation field. In addition, we further model the inverse deformations, enabling the computation of dense correspondences between images generated by our model. Finally, we design an approach to embed real images onto the latent space of our disentangled generative model, enabling editing of real images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tewari_Disentangled3D_Learning_a_3D_Generative_Model_With_Disentangled_Geometry_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tewari_Disentangled3D_Learning_a_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15926 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tewari_Disentangled3D_Learning_a_3D_Generative_Model_With_Disentangled_Geometry_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tewari_Disentangled3D_Learning_a_3D_Generative_Model_With_Disentangled_Geometry_and_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Random Channel Pruning for Neural Network Compression | Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, Luc Van Gool | Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of neural networks. There has been a flurry of algorithms that try to solve this practical problem, each being claimed effective in some ways. Yet, a benchmark to compare those algorithms directly is lacking, mainly due to the complexity of the algorithms and some custom settings such as the particular network configuration or training procedure. A fair benchmark is important for the further development of channel pruning. Meanwhile, recent investigations reveal that the channel configurations discovered by pruning algorithms are at least as important as the pre-trained weights. This gives channel pruning a new role, namely searching the optimal channel configuration. In this paper, we try to determine the channel configuration of the pruned models by random search. The proposed approach provides a new way to compare different methods, namely how well they behave compared with random pruning. We show that this simple strategy works quite well compared with other channel pruning methods. We also show that under this setting, there are surprisingly no clear winners among different channel importance evaluation methods, which then may tilt the research efforts into advanced channel configuration searching methods. Code will be released at https://github.com/ofsoundof/random_channel_pruning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Revisiting_Random_Channel_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.05676 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
One-Bit Active Query With Contrastive Pairs | Yuhang Zhang, Xiaopeng Zhang, Lingxi Xie, Jie Li, Robert C. Qiu, Hengtong Hu, Qi Tian | How to achieve better results with fewer labeling costs remains a challenging task. In this paper, we present a new active learning framework, which for the first time incorporates contrastive learning into recently proposed one-bit supervision. Here one-bit supervision denotes a simple Yes or No query about the correctness of the model's prediction, and is more efficient than previous active learning methods requiring assigning accurate labels to the queried samples. We claim that such one-bit information is intrinsically in accordance with the goal of contrastive loss that pulls positive pairs together and pushes negative samples away. Towards this goal, we design an uncertainty metric to actively select samples for query. These samples are then fed into different branches according to the queried results. The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling. Additionally, we design a negative loss that penalizes the negative samples away from the incorrect predicted class, which can be treated as optimizing hard negatives for the corresponding category. Our method, termed as ObCP, produces a more powerful active learning framework, and experiments on several benchmarks demonstrate its superiority. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_One-Bit_Active_Query_With_Contrastive_Pairs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_One-Bit_Active_Query_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_One-Bit_Active_Query_With_Contrastive_Pairs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_One-Bit_Active_Query_With_Contrastive_Pairs_CVPR_2022_paper.html | CVPR 2022 | null |
Estimating Egocentric 3D Human Pose in the Wild With External Weak Supervision | Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Diogo Luvizon, Christian Theobalt | Egocentric 3D human pose estimation with a single fisheye camera has drawn a significant amount of attention recently. However, existing methods struggle with pose estimation from in-the-wild images, because they can only be trained on synthetic data due to the unavailability of large-scale in-the-wild egocentric datasets. Furthermore, these methods easily fail when the body parts are occluded by or interacting with the surrounding scene. To address the shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured by a head-mounted fisheye camera and an auxiliary external camera, which provides an additional observation of the human body from a third-person perspective during training. We present a new egocentric pose estimation method, which can be trained on the new dataset with weak external supervision. Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision. The pseudo labels are then used to train an egocentric pose estimation network. To facilitate the network training, we propose a novel learning strategy to supervise the egocentric features with the high-quality features extracted by a pretrained external-view pose estimation model. The experiments show that our method predicts accurate 3D poses from a single in-the-wild egocentric image and outperforms the state-of-the-art methods both quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Estimating_Egocentric_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.07929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Estimating_Egocentric_3D_Human_Pose_in_the_Wild_With_External_CVPR_2022_paper.html | CVPR 2022 | null |
Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search | Pengtao Xie, Xuefeng Du | Knowledge distillation has shown great effectiveness for improving neural architecture search (NAS). Mutual knowledge distillation (MKD), where a group of models mutually generate knowledge to train each other, has achieved promising results in many applications. In existing MKD methods, mutual knowledge distillation is performed between models without scrutiny: a worse-performing model is allowed to generate knowledge to train a better-performing model, which may lead to collective failures. To address this problem, we propose a performance-aware MKD (PAMKD) approach for NAS, where knowledge generated by model A is allowed to train model B only if the performance of A is better than B. We propose a three-level optimization framework to formulate PAMKD, where three learning stages are performed end-to-end: 1) each model trains an initial model independently; 2) the initial models are evaluated on a validation set and better-performing models generate knowledge to train worse-performing models; 3) architectures are updated by minimizing a validation loss. Experimental results on a variety of datasets demonstrate that our method is effective. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Performance-Aware_Mutual_Knowledge_Distillation_for_Improving_Neural_Architecture_Search_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Performance-Aware_Mutual_Knowledge_Distillation_for_Improving_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Performance-Aware_Mutual_Knowledge_Distillation_for_Improving_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method | Lai Jiang, Yifei Li, Shengxi Li, Mai Xu, Se Lei, Yichen Guo, Bo Huang | E-commerce images are playing a central role in attracting people's attention when retailing and shopping online, and an accurate attention prediction is of significant importance for both customers and retailers, where its research is yet to start. In this paper, we establish the first dataset of saliency e-commerce images (SalECI), which allows for learning to predict saliency on the e-commerce images. We then provide specialized and thorough analysis by highlighting the distinct features of e-commerce images, e.g., non-locality and correlation to text regions. Correspondingly, taking advantages of the non-local and self-attention mechanisms, we propose a salient SWin-Transformer backbone, followed by a multi-task learning with saliency and text detection heads, where an information flow mechanism is proposed to further benefit both tasks. Experimental results have verified the state-of-the-art performances of our work in the e-commerce scenario. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Does_Text_Attract_Attention_on_E-Commerce_Images_A_Novel_Saliency_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Does_Text_Attract_Attention_on_E-Commerce_Images_A_Novel_Saliency_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Does_Text_Attract_Attention_on_E-Commerce_Images_A_Novel_Saliency_CVPR_2022_paper.html | CVPR 2022 | null |
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction | Shivam Duggal, Deepak Pathak | We present a new framework to learn dense 3D reconstruction and correspondence from a single 2D image. The shape is represented implicitly as deformation over a category-level occupancy field and learned in an unsupervised manner from an unaligned image collection without using any 3D supervision. However, image collections usually contain large intra-category topological variation, e.g. images of different chair instances, posing a major challenge. Hence, prior methods are either restricted only to categories with no topological variation for estimating shape and correspondence or focus only on learning shape independently for each instance without any correspondence. To address this issue, we propose a topologically-aware deformation field that maps 3D points in object space to a higher-dimensional canonical space. Given a single image, we first implicitly deform a 3D point in the object space to a learned category-specific canonical space using the topologically-aware field and then learn the 3D shape in the canonical space. Both the canonical shape and deformation field are trained end-to-end using differentiable rendering via learned recurrent ray marcher. Our approach, dubbed TARS, achieves state-of-the-art reconstruction fidelity on several datasets: ShapeNet, Pascal3D+, CUB, and Pix3D chairs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Duggal_Topologically-Aware_Deformation_Fields_for_Single-View_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duggal_Topologically-Aware_Deformation_Fields_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.06267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duggal_Topologically-Aware_Deformation_Fields_for_Single-View_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duggal_Topologically-Aware_Deformation_Fields_for_Single-View_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
HyperInverter: Improving StyleGAN Inversion via Hypernetwork | Tan M. Dinh, Anh Tuan Tran, Rang Nguyen, Binh-Son Hua | Real-world image manipulation has achieved fantastic progress in recent years as a result of the exploration and utilization of GAN latent spaces. GAN inversion is the first step in this pipeline, which aims to map the real image to the latent code faithfully. Unfortunately, the majority of existing GAN inversion methods fail to meet at least one of the three requirements listed below: high reconstruction quality, editability, and fast inference. We present a novel two-phase strategy in this research that fits all requirements at the same time. In the first phase, we train an encoder to map the input image to StyleGAN2 W-space, which was proven to have excellent editability but lower reconstruction quality. In the second phase, we supplement the reconstruction ability in the initial phase by leveraging a series of hypernetworks to recover the missing information during inversion. These two steps complement each other to yield high reconstruction quality thanks to the hypernetwork branch and excellent editability due to the inversion done in the W-space. Our method is entirely encoder-based, resulting in extremely fast inference. Extensive experiments on two challenging datasets demonstrate the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dinh_HyperInverter_Improving_StyleGAN_Inversion_via_Hypernetwork_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dinh_HyperInverter_Improving_StyleGAN_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dinh_HyperInverter_Improving_StyleGAN_Inversion_via_Hypernetwork_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dinh_HyperInverter_Improving_StyleGAN_Inversion_via_Hypernetwork_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse Non-Local CRF | Olga Veksler, Yuri Boykov | CRF is a classical computer vision model which is also useful for deep learning. There are two common CRF types: sparse and dense. Sparse CRF connects only the nearby pixels, while dense CRF has global connectivity. Therefore dense CRF is a more general model, but it is much harder to optimize compared to sparse CRF. In fact, only a certain form of dense CRF is optimized in practice, and even then approximately. We propose a new sparse non-local CRF: it has a sparse number of connections, but it has both local and non-local ones. Like sparse CRF, the total number of connections is small, and our model is easy to optimize exactly. Like dense CRF, our model is more general than sparse CRF due to non-local connections. We show that our sparse non-local CRF can model properties similar to that of the popular Gaussian edge dense CRF. Besides efficiency, another advantage is that our edge weights are less restricted compared to Gaussian edge dense CRF. We design models that take advantage of this flexibility. We also discuss connection of our model to other CRF models. Finally, to prove the usefulness of our model, we evaluate it on the classical application of segmentation from a bounding box and for deep learning based salient object segmentation. We improve state of the art for both applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Veksler_Sparse_Non-Local_CRF_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Veksler_Sparse_Non-Local_CRF_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Veksler_Sparse_Non-Local_CRF_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Veksler_Sparse_Non-Local_CRF_CVPR_2022_paper.html | CVPR 2022 | null |
Dataset Distillation by Matching Training Trajectories | George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu | Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. The task is extremely challenging as it often involves backpropagating through the full training process or assuming the strong constraint that a single training step on distilled data can only imitate a single step on real data. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill with higher-resolution visual data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cazenavette_Dataset_Distillation_by_Matching_Training_Trajectories_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.11932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cazenavette_Dataset_Distillation_by_Matching_Training_Trajectories_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cazenavette_Dataset_Distillation_by_Matching_Training_Trajectories_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Driving-Oriented Metric for Lane Detection Models | Takami Sato, Qi Alfred Chen | After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods. While they have played a major role in improving the performance of lane detection methods, the validity of this evaluation method in downstream tasks has not been adequately researched. In this study, we design 2 new driving-oriented metrics for lane detection: End-to-End Lateral Deviation metric (E2E-LD) is directly formulated based on the requirements of autonomous driving, a core task downstream of lane detection; Per-frame Simulated Lateral Deviation metric (PSLD) is a lightweight surrogate metric of E2E-LD. To evaluate the validity of the metrics, we conduct a large-scale empirical study with 4 major types of lane detection approaches on the TuSimple dataset and our newly constructed dataset Comma2k19-LD. Our results show that the conventional metrics have strongly negative correlations (<=-0.55) with E2E-LD, meaning that some recent improvements purely targeting the conventional metrics may not have led to meaningful improvements in autonomous driving, but rather may actually have made it worse by overfitting to the conventional metrics. On the contrary, PSLD shows statistically significant strong positive correlations (>=0.38) with E2E-LD. As a result, the conventional metrics tend to overestimate less robust models. As autonomous driving is a security/safety-critical system, the underestimation of robustness hinders the sound development of practical lane detection models. We hope that our study will help the community achieve more downstream task-aware evaluations for lane detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sato_Towards_Driving-Oriented_Metric_for_Lane_Detection_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sato_Towards_Driving-Oriented_Metric_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16851 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sato_Towards_Driving-Oriented_Metric_for_Lane_Detection_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sato_Towards_Driving-Oriented_Metric_for_Lane_Detection_Models_CVPR_2022_paper.html | CVPR 2022 | null |
EPro-PnP: Generalized End-to-End Probabilistic Perspective-N-Points for Monocular Object Pose Estimation | Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, Hao Li | Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagating the gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D points from scratch fails to converge with existing approaches, since the deterministic pose is inherently non-differentiable. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose on the SE(3) manifold, essentially bringing categorical Softmax to the continuous domain. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle unifies the existing approaches and resembles the attention mechanism. EPro-PnP significantly outperforms competitive baselines, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D object detection benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_EPro-PnP_Generalized_End-to-End_Probabilistic_Perspective-N-Points_for_Monocular_Object_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_EPro-PnP_Generalized_End-to-End_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_EPro-PnP_Generalized_End-to-End_Probabilistic_Perspective-N-Points_for_Monocular_Object_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_EPro-PnP_Generalized_End-to-End_Probabilistic_Perspective-N-Points_for_Monocular_Object_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection | Yibo Zhou | In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstruction error as a metric of novelty vs. normality. We formulate the essence of such approach as a quadruplet domain translation with an intrinsic bias to only query for a proxy of conditional data uncertainty. Accordingly, an improvement direction is formalized as maximumly compressing the autoencoder's latent space while ensuring its reconstructive power for acting as a described domain translator. From it, strategies are introduced including semantic reconstruction, data certainty decomposition and normalized L2 distance to substantially improve original methods, which together establish state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method works without any additional data, hard-to-implement structure, time-consuming pipeline, and even harming the classification accuracy of known classes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Rethinking_Reconstruction_Autoencoder-Based_Out-of-Distribution_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Rethinking_Reconstruction_Autoencoder-Based_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.02194 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Rethinking_Reconstruction_Autoencoder-Based_Out-of-Distribution_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Rethinking_Reconstruction_Autoencoder-Based_Out-of-Distribution_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
XYDeblur: Divide and Conquer for Single Image Deblurring | Seo-Won Ji, Jeongmin Lee, Seung-Wook Kim, Jun-Pyo Hong, Seung-Jin Baek, Seung-Won Jung, Sung-Jea Ko | Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images. Having long been proven to be effective in image restoration tasks, a single lane of encoder-decoder architecture overlooks the characteristic of deblurring, where a blurry image is generated from complicated blur kernels caused by tangled motions. Toward an effective network architecture, we present complemental sub-solutions learning with a one-encoder-two-decoder architecture for single image deblurring. Observing that multiple decoders successfully learn to decompose information in the encoded features into directional components, we further improve both the network efficiency and the deblurring performance by rotating and sharing kernels exploited in the decoders, which prevents the decoders from separating unnecessary components such as color shift. As a result, our proposed network shows superior results as compared to U-Net while preserving the network parameters, and the use of the proposed network as the base network can improve the performance of existing state-of-the-art deblurring networks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ji_XYDeblur_Divide_and_Conquer_for_Single_Image_Deblurring_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ji_XYDeblur_Divide_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_XYDeblur_Divide_and_Conquer_for_Single_Image_Deblurring_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ji_XYDeblur_Divide_and_Conquer_for_Single_Image_Deblurring_CVPR_2022_paper.html | CVPR 2022 | null |
Generating Diverse and Natural 3D Human Motions From Text | Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, Li Cheng | Automated generation of 3D human motions from text is a challenging problem. The generated motions are expected to be sufficiently diverse to explore the text-grounded motion space, and more importantly, accurately depicting the content in prescribed text descriptions. Here we tackle this problem with a two-stage approach: text2length sampling and text2motion generation. Text2length involves sampling from the learned distribution function of motion lengths conditioned on the input text. This is followed by our text2motion module using temporal variational autoencoder to synthesize a diverse set of human motions of the sampled lengths. Instead of directly engaging with pose sequences, we propose motion snippet code as our internal motion representation, which captures local semantic motion contexts and is empirically shown to facilitate the generation of plausible motions faithful to the input text. Moreover, a large-scale dataset of scripted 3D Human motions, HumanML3D, is constructed, consisting of 14,616 motion clips and 44,970 text descriptions. Extensive empirical experiments demonstrate the effectiveness of our approach. Project webpage: https://ericguo5513.github.io/text-to-motion/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Generating_Diverse_and_Natural_3D_Human_Motions_From_Text_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Generating_Diverse_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Generating_Diverse_and_Natural_3D_Human_Motions_From_Text_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Generating_Diverse_and_Natural_3D_Human_Motions_From_Text_CVPR_2022_paper.html | CVPR 2022 | null |
E-CIR: Event-Enhanced Continuous Intensity Recovery | Chen Song, Qixing Huang, Chandrajit Bajaj | A camera begins to sense light the moment we press the shutter button. During the exposure interval, relative motion between the scene and the camera causes motion blur, a common undesirable visual artifact. This paper presents E-CIR, which converts a blurry image into a sharp video represented as a parametric function from time to intensity. E-CIR leverages events as an auxiliary input. We discuss how to exploit the temporal event structure to construct the parametric bases. We demonstrate how to train a deep learning model to predict the function coefficients. To improve the appearance consistency, we further introduce a refinement module to propagate visual features among consecutive frames. Compared to state-of-the-art event-enhanced deblurring approaches, E-CIR generates smoother and more realistic results. The implementation of E-CIR is available at https://github.com/chensong1995/E-CIR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_E-CIR_Event-Enhanced_Continuous_Intensity_Recovery_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_E-CIR_Event-Enhanced_Continuous_Intensity_Recovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_E-CIR_Event-Enhanced_Continuous_Intensity_Recovery_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond | Yi Yu, Wenhan Yang, Yap-Peng Tan, Alex C. Kot | Rain removal aims to remove rain streaks from images/videos and reduce the disruptive effects caused by rain. It not only enhances image/video visibility but also allows many computer vision algorithms to function properly. This paper makes the first attempt to conduct a comprehensive study on the robustness of deep learning-based rain removal methods against adversarial attacks. Our study shows that, when the image/video is highly degraded, rain removal methods are more vulnerable to the adversarial attacks as small distortions/perturbations become less noticeable or detectable. In this paper, we first present a comprehensive empirical evaluation of various methods at different levels of attacks and with various losses/targets to generate the perturbations from the perspective of human perception and machine analysis tasks. A systematic evaluation of key modules in existing methods is performed in terms of their robustness against adversarial attacks. From the insights of our analysis, we construct a more robust deraining method by integrating these effective modules. Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible; 2) object-sensitive attacks, adding perturbations only in regions near the given objects. Code is available at https://github.com/yuyi-sd/Robust_Rain_Removal. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Towards_Robust_Rain_Removal_Against_Adversarial_Attacks_A_Comprehensive_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Towards_Robust_Rain_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.16931 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Towards_Robust_Rain_Removal_Against_Adversarial_Attacks_A_Comprehensive_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Towards_Robust_Rain_Removal_Against_Adversarial_Attacks_A_Comprehensive_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes | Peishan Cong, Xinge Zhu, Feng Qiao, Yiming Ren, Xidong Peng, Yuenan Hou, Lan Xu, Ruigang Yang, Dinesh Manocha, Yuexin Ma | Accurately detecting and tracking pedestrians in 3D space is challenging due to large variations in rotations, poses and scales. The situation becomes even worse for dense crowds with severe occlusions. However, existing benchmarks either only provide 2D annotations, or have limited 3D annotations with low-density pedestrian distribution, making it difficult to build a reliable pedestrian perception system especially in crowded scenes. To better evaluate pedestrian perception algorithms in crowded scenarios, we introduce a large-scale multimodal dataset, STCrowd. Specifically, in STCrowd, there are a total of 219K pedestrian instances and 20 persons per frame on average, with various levels of occlusion. We provide synchronized LiDAR point clouds and camera images as well as their corresponding 3D labels and joint IDs. STCrowd can be used for various tasks, including LiDAR-only, image-only, and sensor-fusion based pedestrian detection and tracking. We provide baselines for most of the tasks. In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes. Extensive experiments show that our new method achieves state-of-the-art performance on the STCrowd dataset, especially on cases with severe occlusion. The dataset and code will be released to facilitate related research when the paper is published. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cong_STCrowd_A_Multimodal_Dataset_for_Pedestrian_Perception_in_Crowded_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cong_STCrowd_A_Multimodal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cong_STCrowd_A_Multimodal_Dataset_for_Pedestrian_Perception_in_Crowded_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cong_STCrowd_A_Multimodal_Dataset_for_Pedestrian_Perception_in_Crowded_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Decomposition for Stochastic Normal-Abnormal Transport | Peirong Liu, Yueh Lee, Stephen Aylward, Marc Niethammer | Advection-diffusion equations describe a large family of natural transport processes, e.g., fluid flow, heat transfer, and wind transport. They are also used for optical flow and perfusion imaging computations. We develop a machine learning model, D^2-SONATA, built upon a stochastic advection-diffusion equation, which predicts the velocity and diffusion fields that drive 2D/3D image time-series of transport. In particular, our proposed model incorporates a model of transport atypicality, which isolates abnormal differences between expected normal transport behavior and the observed transport. In a medical context such a normal-abnormal decomposition can be used, for example, to quantify pathologies. Specifically, our model identifies the advection and diffusion contributions from the transport time-series and simultaneously predicts an anomaly value field to provide a decomposition into normal and abnormal advection and diffusion behavior. To achieve improved estimation performance for the velocity and diffusion-tensor fields underlying the advection-diffusion process and for the estimation of the anomaly fields, we create a 2D/3D anomaly-encoded advection-diffusion simulator, which allows for supervised learning. We further apply our model on a brain perfusion dataset from ischemic stroke patients via transfer learning. Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Deep_Decomposition_for_Stochastic_Normal-Abnormal_Transport_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Deep_Decomposition_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Deep_Decomposition_for_Stochastic_Normal-Abnormal_Transport_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Deep_Decomposition_for_Stochastic_Normal-Abnormal_Transport_CVPR_2022_paper.html | CVPR 2022 | null |
Global Context With Discrete Diffusion in Vector Quantised Modelling for Image Generation | Minghui Hu, Yujie Wang, Tat-Jen Cham, Jianfei Yang, P.N. Suganthan | The integration of Vector Quantised Variational AutoEncoder (VQ-VAE) with autoregressive models as generation part has yielded high-quality results on image generation. However, the autoregressive models will strictly follow the progressive scanning order during the sampling phase. This leads the existing VQ series models to hardly escape the trap of lacking global information. Denoising Diffusion Probabilistic Models (DDPM) in the continuous domain have shown a capability to capture the global context, while generating high-quality images. In the discrete state space, some works have demonstrated the potential to perform text generation and low resolution image generation. We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space. Meanwhile, the integration of the discrete VAE with the diffusion model resolves the drawback of conventional autoregressive models being oversized, and the diffusion model which demands excessive time in the sampling process when generating images. It is found that the quality of the generated images is heavily dependent on the discrete visual codebook. Extensive experiments demonstrate that the proposed Vector Quantised Discrete Diffusion Model (VQ-DDM) is able to achieve comparable performance to top-tier methods with low complexity. It also demonstrates outstanding advantages over other vectors quantised with autoregressive models in terms of image inpainting tasks without additional training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Global_Context_With_Discrete_Diffusion_in_Vector_Quantised_Modelling_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Global_Context_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Global_Context_With_Discrete_Diffusion_in_Vector_Quantised_Modelling_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Global_Context_With_Discrete_Diffusion_in_Vector_Quantised_Modelling_for_CVPR_2022_paper.html | CVPR 2022 | null |
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation | Nathaniel Merrill, Yuliang Guo, Xingxing Zuo, Xinyu Huang, Stefan Leutenegger, Xi Peng, Liu Ren, Guoquan Huang | We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose information from SLAM to provide prior knowledge for tracking keypoints on symmetric objects - ensuring that new measurements are consistent with the current 3D scene. Moreover, our semantic keypoint network is trained to predict the Gaussian covariance for the keypoints that captures the true error of the prediction, and thus is not only useful as a weight for the residuals in the system's optimization problems, but also as a means to detect harmful statistical outliers without choosing a manual threshold. Experiments show that our method provides competitive performance to the state of the art in 6DoF object pose estimation, and at a real-time speed. Our code, pre-trained models, and keypoint labels are available https://github.com/rpng/suo_slam. | https://openaccess.thecvf.com/content/CVPR2022/papers/Merrill_Symmetry_and_Uncertainty-Aware_Object_SLAM_for_6DoF_Object_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Merrill_Symmetry_and_Uncertainty-Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.01823 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Merrill_Symmetry_and_Uncertainty-Aware_Object_SLAM_for_6DoF_Object_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Merrill_Symmetry_and_Uncertainty-Aware_Object_SLAM_for_6DoF_Object_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception | Shaoyu Chen, Xinggang Wang, Tianheng Cheng, Wenqiang Zhang, Qian Zhang, Chang Huang, Wenyu Liu | Studying the inherent symmetry of data is of great importance in machine learning. Point cloud, the most important data format for 3D environmental perception, is naturally endowed with strong radial symmetry. In this work, we exploit this radial symmetry via a divide-and-conquer strategy to boost 3D perception performance and ease optimization. We propose Azimuth Normalization (AziNorm), which normalizes the point clouds along the radial direction and eliminates the variability brought by the difference of azimuth. AziNorm can be flexibly incorporated into most LiDAR-based perception methods. To validate its effectiveness and generalization ability, we apply AziNorm in both object detection and semantic segmentation. For detection, we integrate AziNorm into two representative detection methods, the one-stage SECOND detector and the state-of-the-art two-stage PV-RCNN detector. Experiments on Waymo Open Dataset demonstrate that AziNorm improves SECOND and PV-RCNN by 7.03 mAPH and 3.01 mAPH respectively. For segmentation, we integrate AziNorm into KPConv. On SemanticKitti dataset, AziNorm improves KPConv by 1.6/1.1 mIoU on val/test set. Besides, AziNorm remarkably improves data efficiency and accelerates convergence, reducing the requirement of data amounts or training epochs by an order of magnitude. SECOND w/ AziNorm can significantly outperform fully trained vanilla SECOND, even trained with only 10% data or 10% epochs. Code and models are available at https://github.com/hustvl/AziNorm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_AziNorm_Exploiting_the_Radial_Symmetry_of_Point_Cloud_for_Azimuth-Normalized_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_AziNorm_Exploiting_the_Radial_Symmetry_of_Point_Cloud_for_Azimuth-Normalized_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_AziNorm_Exploiting_the_Radial_Symmetry_of_Point_Cloud_for_Azimuth-Normalized_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Multimodal Depth Estimation From Light Fields | Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother | Light field applications, especially light field rendering and depth estimation, developed rapidly in recent years. While state-of-the-art light field rendering methods handle semi-transparent and reflective objects well, depth estimation methods either ignore these cases altogether or only deliver a weak performance. We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel. Based on the simple idea of outputting a posterior depth distribution instead of only a single estimate, we develop and explore several different deep-learning-based approaches to the problem. Additionally, we contribute the first "multimodal light field depth dataset" that contains the depths of all objects which contribute to the color of a pixel. This allows us to supervise the multimodal depth prediction and also validate all methods by measuring the KL divergence of the predicted posteriors. With our thorough analysis and novel dataset, we aim to start a new line of depth estimation research that overcomes some of the long-standing limitations of this field. | https://openaccess.thecvf.com/content/CVPR2022/papers/Leistner_Towards_Multimodal_Depth_Estimation_From_Light_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Leistner_Towards_Multimodal_Depth_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16542 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Leistner_Towards_Multimodal_Depth_Estimation_From_Light_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Leistner_Towards_Multimodal_Depth_Estimation_From_Light_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Recognize Procedural Activities With Distant Supervision | Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, Lorenzo Torresani | In this paper we consider the problem of classifying fine-grained, multi-step activities (e.g., cooking different recipes, making disparate home improvements, creating various forms of arts and crafts) from long videos spanning up to several minutes. Accurately categorizing these activities requires not only recognizing the individual steps that compose the task but also capturing their temporal dependencies. This problem is dramatically different from traditional action classification, where models are typically optimized on videos that span only a few seconds and that are manually trimmed to contain simple atomic actions. While step annotations could enable the training of models to recognize the individual steps of procedural activities, existing large-scale datasets in this area do not include such segment labels due to the prohibitive cost of manually annotating temporal boundaries in long videos. To address this issue, we propose to automatically identify steps in instructional videos by leveraging the distant supervision of a textual knowledge base (wikiHow) that includes detailed descriptions of the steps needed for the execution of a wide variety of complex activities. Our method uses a language model to match noisy, automatically-transcribed speech from the video to step descriptions in the knowledge base. We demonstrate that video models trained to recognize these automatically-labeled steps (without manual supervision) yield a representation that achieves superior generalization performance on four downstream tasks: recognition of procedural activities, step classification, step forecasting and egocentric video classification. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Learning_To_Recognize_Procedural_Activities_With_Distant_Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Learning_To_Recognize_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.10990 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Learning_To_Recognize_Procedural_Activities_With_Distant_Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Learning_To_Recognize_Procedural_Activities_With_Distant_Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
Multimodal Material Segmentation | Yupeng Liang, Ryosuke Wakaki, Shohei Nobuhara, Ko Nishino | Recognition of materials from their visual appearance is essential for computer vision tasks, especially those that involve interaction with the real world. Material segmentation, i.e., dense per-pixel recognition of materials, remains challenging as, unlike objects, materials do not exhibit clearly discernible visual signatures in their regular RGB appearances. Different materials, however, do lead to different radiometric behaviors, which can often be captured with non-RGB imaging modalities. We realize multimodal material segmentation from RGB, polarization, and near-infrared images. We introduce the MCubeS dataset (from MultiModal Material Segmentation) which contains 500 sets of multimodal images capturing 42 street scenes. Ground truth material segmentation as well as semantic segmentation are annotated for every image and pixel. We also derive a novel deep neural network, MCubeSNet, which learns to focus on the most informative combinations of imaging modalities for each material class with a newly derived region-guided filter selection (RGFS) layer. We use semantic segmentation, as a prior to "guide" this filter selection. To the best of our knowledge, our work is the first comprehensive study on truly multimodal material segmentation. We believe our work opens new avenues of practical use of material information in safety critical applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Multimodal_Material_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Multimodal_Material_Segmentation_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Multimodal_Material_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Multimodal_Material_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Frame Self-Supervised Depth With Transformers | Vitor Guizilini, Rareș Ambruș, Dian Chen, Sergey Zakharov, Adrien Gaidon | Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearance-based features. In this paper we revisit feature matching for self-supervised monocular depth estimation, and propose a novel transformer architecture for cost volume generation. We use depth-discretized epipolar sampling to select matching candidates, and refine predictions through a series of self- and cross-attention layers. These layers sharpen the matching probability between pixel features, improving over standard similarity metrics prone to ambiguities and local minima. The refined cost volume is decoded into depth estimates, and the whole pipeline is trained end-to-end from videos using only a photometric objective. Experiments on the KITTI and DDAD datasets show that our DepthFormer architecture establishes a new state of the art in self-supervised monocular depth estimation, and is even competitive with highly specialized supervised single-frame architectures. We also show that our learned cross-attention network yields representations transferable across datasets, increasing the effectiveness of pre-training strategies. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guizilini_Multi-Frame_Self-Supervised_Depth_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guizilini_Multi-Frame_Self-Supervised_Depth_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guizilini_Multi-Frame_Self-Supervised_Depth_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guizilini_Multi-Frame_Self-Supervised_Depth_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Rotation-Invariant Aerial Object Detection Network | Xiaoxu Feng, Xiwen Yao, Gong Cheng, Junwei Han | Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images. Existing predominant WSOD approaches built on regular CNNs which are not inherently designed to tackle object rotations without corresponding constraints, thereby leading to rotation-sensitive object detector. Meanwhile, current solutions have been prone to fall into the issue with unstable detectors, as they ignore lower-scored instances and may regard them as backgrounds. To address these issues, in this paper, we construct a novel end-to-end weakly supervised Rotation-Invariant aerial object detection Network (RINet). It is implemented with a flexible multi-branch online detector refinement, to be naturally more rotation-perceptive against oriented objects. Specifically, RINet first performs label propagating from the predicted instances to their rotated ones in a progressive refinement manner. Meanwhile, we propose to couple the predicted instance labels among different rotation-perceptive branches for generating rotation-consistent supervision and meanwhile pursuing all possible instances. With the rotation-consistent supervisions, RINet enforces and encourages consistent yet complementary feature learning for WSOD without additional annotations and hyper-parameters. On the challenging NWPU VHR-10.v2 and DIOR datasets, extensive experiments clearly demonstrate that we significantly boost existing WSOD methods to a new state-of-the-art performance. The code will be available at: https://github.com/XiaoxFeng/RINet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Weakly_Supervised_Rotation-Invariant_Aerial_Object_Detection_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Modeling Motion With Multi-Modal Features for Text-Based Video Segmentation | Wangbo Zhao, Kai Wang, Xiangxiang Chu, Fuzhao Xue, Xinchao Wang, Yang You | Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appearance, motion, and linguistic features to achieve accurate segmentation. Specifically, we propose a multi-modal video transformer, which can fuse and aggregate multi-modal and temporal features between frames. Furthermore, we design a language-guided feature fusion module to progressively fuse appearance and motion features in each feature level with guidance from linguistic features. Finally, a multi-modal alignment loss is proposed to alleviate the semantic gap between features from different modalities. Extensive experiments on A2D Sentences and J-HMDB Sentences verify the performance and the generalization ability of our method compared to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Modeling_Motion_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Surface Reconstruction From Point Clouds by Learning Predictive Context Priors | Baorui Ma, Yu-Shen Liu, Matthias Zwicker, Zhizhong Han | Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented as neural network-based signed distance functions (SDFs) with some parameters encoding the local contexts. To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location. However, this requires the local context prior to generalize to a wide variety of unseen target regions, which is hard to achieve. To resolve this issue, we introduce Predictive Context Priors by learning Predictive Queries for each specific point cloud at inference time. Specifically, we first train a local context prior using a large point cloud dataset similar to previous techniques. For surface reconstruction at inference time, however, we specialize the local context prior into our Predictive Context Prior by learning Predictive Queries, which predict adjusted spatial query locations as displacements of the original locations. This leads to a global SDF that fits the specific point cloud the best. Intuitively, the query prediction enables us to flexibly search the learned local context prior over the entire prior space, rather than being restricted to the fixed query locations, and this improves the generalizability. Our method does not require ground truth signed distances, normals, or any additional procedure of signed distance fusion across overlapping regions. Our experimental results in surface reconstruction for single shapes or complex scenes show significant improvements over the state-of-the-art under widely used benchmarks. Code and data are available at https://github.com/mabaorui/PredictableContextPrior. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Surface_Reconstruction_From_Point_Clouds_by_Learning_Predictive_Context_Priors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Surface_Reconstruction_From_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.11015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Surface_Reconstruction_From_Point_Clouds_by_Learning_Predictive_Context_Priors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Surface_Reconstruction_From_Point_Clouds_by_Learning_Predictive_Context_Priors_CVPR_2022_paper.html | CVPR 2022 | null |
Deformable Video Transformer | Jue Wang, Lorenzo Torresani | Video transformers have recently emerged as an effective alternative to convolutional networks for action classification. However, most prior video transformers adopt either global space-time attention or hand-defined strategies to compare patches within and across frames. These fixed attention schemes not only have high computational cost but, by comparing patches at predetermined locations, they neglect the motion dynamics in the video. In this paper, we introduce the Deformable Video Transformer (DVT), which dynamically predicts a small subset of video patches to attend for each query location based on motion information, thus allowing the model to decide where to look in the video based on correspondences across frames. Crucially, these motion-based correspondences are obtained at zero-cost from information stored in the compressed format of the video. Our deformable attention mechanism is optimized directly with respect to classification performance, thus eliminating the need for suboptimal hand-design of attention strategies. Experiments on four large-scale video benchmarks (Kinetics-400, Something-Something-V2, EPIC-KITCHENS and Diving-48) demonstrate that, compared to existing video transformers, our model achieves higher accuracy at the same or lower computational cost, and it attains state-of-the-art results on these four datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Deformable_Video_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16795 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Deformable_Video_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Deformable_Video_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Keypoint Discovery in Behavioral Videos | Jennifer J. Sun, Serim Ryou, Roni H. Goldshmid, Brandon Weissbourd, John O. Dabiri, David J. Anderson, Ann Kennedy, Yisong Yue, Pietro Perona | We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requiring manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our discovered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on keypoint regression among self-supervised methods. Additionally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior classification, suggesting that our method can dramatically reduce model training costs vis-a-vis supervised methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Self-Supervised_Keypoint_Discovery_in_Behavioral_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Self-Supervised_Keypoint_Discovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Self-Supervised_Keypoint_Discovery_in_Behavioral_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Self-Supervised_Keypoint_Discovery_in_Behavioral_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes | Rui Zhu, Zhengqin Li, Janarbek Matai, Fatih Porikli, Manmohan Chandraker | Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Shadows, highlights, and inter-reflections caused by visible and invisible light sources require reasoning about long-range interactions for inverse rendering, which seeks to recover the components of image formation, namely, shape, material, and lighting. In this work, our intuition is that the long-range attention learned by transformer architectures is ideally suited to solve longstanding challenges in single-image inverse rendering. We demonstrate with a specific instantiation of a dense vision transformer, \Ours , that excels at both single-task and multi-task reasoning required for inverse rendering. Specifically, we propose a transformer architecture to simultaneously estimate depths, normals, spatially-varying albedo, roughness and lighting from a single image of an indoor scene. Our extensive evaluations on benchmark datasets demonstrate state-of-the-art results on each of the above tasks, enabling applications like object insertion and material editing in a single unconstrained real image, with greater photorealism than prior works. Code and data are publicly released at https://github.com/ViLab-UCSD/IRISformer | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_IRISformer_Dense_Vision_Transformers_for_Single-Image_Inverse_Rendering_in_Indoor_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_IRISformer_Dense_Vision_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_IRISformer_Dense_Vision_Transformers_for_Single-Image_Inverse_Rendering_in_Indoor_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_IRISformer_Dense_Vision_Transformers_for_Single-Image_Inverse_Rendering_in_Indoor_CVPR_2022_paper.html | CVPR 2022 | null |
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation | Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Andrés Camero, Jingliang Hu, Ariadna Pregel Hoderlein, Çağlar Şenaras, Timothy Davis, Daniel Cremers, Giovanni Marchisio, Xiao Xiang Zhu, Laura Leal-Taixé | Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires both time-series data and pixel-wise segmentations. To that end, we propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest distributed over the globe with imagery from Planet Labs. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes. DynamicEarthNet is the first dataset that provides this unique combination of daily measurements and high-quality labels. In our experiments, we compare several established baselines that either utilize the daily observations as additional training data (semi-supervised learning) or multiple observations at once (spatio-temporal learning) as a point of reference for future research. Finally, we propose a new evaluation metric SCS that addresses the specific challenges associated with time-series semantic change segmentation. The data is available at: https://mediatum.ub.tum.de/1650201. | https://openaccess.thecvf.com/content/CVPR2022/papers/Toker_DynamicEarthNet_Daily_Multi-Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Toker_DynamicEarthNet_Daily_Multi-Spectral_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12560 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Toker_DynamicEarthNet_Daily_Multi-Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Toker_DynamicEarthNet_Daily_Multi-Spectral_Satellite_Dataset_for_Semantic_Change_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Connecting the Complementary-View Videos: Joint Camera Identification and Subject Association | Ruize Han, Yiyang Gan, Jiacheng Li, Feifan Wang, Wei Feng, Song Wang | We attempt to connect the data from complementary views, i.e., top view from drone-mounted cameras in the air, and side view from wearable cameras on the ground. Collaborative analysis of such complementary-view data can facilitate to build the air-ground cooperative visual system for various kinds of applications. This is a very challenging problem due to the large view difference between top and side views. In this paper, we develop a new approach that can simultaneously handle three tasks: i) localizing the side-view camera in the top view; ii) estimating the view direction of the side-view camera; iii) detecting and associating the same subjects on the ground across the complementary views. Our main idea is to explore the spatial position layout of the subjects in two views. In particular, we propose a spatial-aware position representation method to embed the spatial-position distribution of the subjects in different views. We further design a cross-view video collaboration framework composed of a camera identification module and a subject association module to simultaneously perform the above three tasks. We collect a new synthetic dataset consisting of top-view and side-view video sequence pairs for performance evaluation and the experimental results show the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Connecting_the_Complementary-View_Videos_Joint_Camera_Identification_and_Subject_Association_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Connecting_the_Complementary-View_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Connecting_the_Complementary-View_Videos_Joint_Camera_Identification_and_Subject_Association_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Connecting_the_Complementary-View_Videos_Joint_Camera_Identification_and_Subject_Association_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps | Ke Guo, Wenxi Liu, Jia Pan | In this paper, we aim to forecast a future trajectory distribution of a moving agent in the real world, given the social scene images and historical trajectories. Yet, it is a challenging task because the ground-truth distribution is unknown and unobservable, while only one of its samples can be applied for supervising model learning, which is prone to bias. Most recent works focus on predicting diverse trajectories in order to cover all modes of the real distribution, but they may despise the precision and thus give too much credit to unrealistic predictions. To address the issue, we learn the distribution with symmetric cross-entropy using occupancy grid maps as an explicit and scene-compliant approximation to the ground-truth distribution, which can effectively penalize unlikely predictions. In specific, we present an inverse reinforcement learning based multi-modal trajectory distribution forecasting framework that learns to plan by an approximate value iteration network in an end-to-end manner. Besides, based on the predicted distribution, we generate a small set of representative trajectories through a differentiable Transformer-based network, whose attention mechanism helps to model the relations of trajectories. In experiments, our method achieves state-of-the-art performance on the Stanford Drone Dataset and Intersection Drone Dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_End-to-End_Trajectory_Distribution_Prediction_Based_on_Occupancy_Grid_Maps_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_End-to-End_Trajectory_Distribution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_End-to-End_Trajectory_Distribution_Prediction_Based_on_Occupancy_Grid_Maps_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_End-to-End_Trajectory_Distribution_Prediction_Based_on_Occupancy_Grid_Maps_CVPR_2022_paper.html | CVPR 2022 | null |
Fast, Accurate and Memory-Efficient Partial Permutation Synchronization | Shaohan Li, Yunpeng Shi, Gilad Lerman | Previous partial permutation synchronization (PPS) algorithms, which are commonly used for multi-object matching, often involve computation-intensive and memory-demanding matrix operations. These operations become intractable for large scale structure-from-motion datasets. For pure permutation synchronization, the recent Cycle-Edge Message Passing (CEMP) framework suggests a memory-efficient and fast solution. Here we overcome the restriction of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for estimating the corruption levels of the observed partial permutations. It allows us to subsequently implement a nonconvex weighted projected power method without the need of spectral initialization. The resulting new PPS algorithm, MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse matrix operations, and thus enjoys lower time and space complexities in comparison to previous PPS algorithms. We prove that under adversarial corruption, though without additive noise and with certain assumptions, CEMP-Partial is able to exactly classify corrupted and clean partial permutations. We demonstrate the state-of-the-art accuracy, speed and memory efficiency of our method on both synthetic and real datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Fast_Accurate_and_Memory-Efficient_Partial_Permutation_Synchronization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Fast_Accurate_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16505 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Fast_Accurate_and_Memory-Efficient_Partial_Permutation_Synchronization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Fast_Accurate_and_Memory-Efficient_Partial_Permutation_Synchronization_CVPR_2022_paper.html | CVPR 2022 | null |
Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging | Lingen Li, Lizhi Wang, Weitao Song, Lei Zhang, Zhiwei Xiong, Hua Huang | Diffractive snapshot hyperspectral imaging based on the deep optics framework has been striving to capture the spectral images of dynamic scenes. However, existing deep optics frameworks all suffer from the mismatch between the optical hardware and the reconstruction algorithm due to the quantization operation in the diffractive optical element (DOE) fabrication, leading to the limited performance of hyperspectral imaging in practice. In this paper, we propose the quantization-aware deep optics for diffractive snapshot hyperspectral imaging. Our key observation is that common lithography techniques used in fabricating DOEs need to quantize the DOE height map to a few levels, and can freely set the height for each level. Therefore, we propose to integrate the quantization operation into the DOE height map optimization and design an adaptive mechanism to adjust the physical height of each quantization level. According to the optimization, we fabricate the quantized DOE directly and build a diffractive hyperspectral snapshot imaging system. Our method develops the deep optics framework to be more practical through the awareness of and adaptation to the quantization operation of the DOE physical structure, making the fabricated DOE and the reconstruction algorithm match each other systematically. Extensive synthetic simulation and real hardware experiments validate the superior performance of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Quantization-Aware_Deep_Optics_for_Diffractive_Snapshot_Hyperspectral_Imaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Quantization-Aware_Deep_Optics_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Quantization-Aware_Deep_Optics_for_Diffractive_Snapshot_Hyperspectral_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Quantization-Aware_Deep_Optics_for_Diffractive_Snapshot_Hyperspectral_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation | Linjiang Huang, Liang Wang, Hongsheng Li | Weakly supervised temporal action localization targets at localizing temporal boundaries of actions and simultaneously identify their categories with only video-level category labels. Many existing methods seek to generate pseudo labels for bridging the discrepancy between classification and localization, but usually only make use of limited contextual information for pseudo label generation. To alleviate this problem, we propose a representative snippet summarization and propagation framework. Our method seeks to mine the representative snippets in each video for better propagating information between video snippets. For each video, its own representative snippets and the representative snippets from a memory bank are propagated to update the input features in an intra- and inter-video manner. The pseudo labels are generated from the temporal class activation maps of the updated features to rectify the predictions of the main branch. Our method obtains superior performance in comparison to the existing methods on two benchmarks, THUMOS14 and ActivityNet1.3, achieving gains as high as 1.2% in terms of average mAP on THUMOS14. Our code is available at https://github.com/LeonHLJ/RSKP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Weakly_Supervised_Temporal_Action_Localization_via_Representative_Snippet_Knowledge_Propagation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Weakly_Supervised_Temporal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02925 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Weakly_Supervised_Temporal_Action_Localization_via_Representative_Snippet_Knowledge_Propagation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Weakly_Supervised_Temporal_Action_Localization_via_Representative_Snippet_Knowledge_Propagation_CVPR_2022_paper.html | CVPR 2022 | null |
Parametric Scattering Networks | Shanel Gauthier, Benjamin Thérien, Laurent Alsène-Racicot, Muawiz Chaudhary, Irina Rish, Eugene Belilovsky, Michael Eickenberg, Guy Wolf | The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yield more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gauthier_Parametric_Scattering_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gauthier_Parametric_Scattering_Networks_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gauthier_Parametric_Scattering_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gauthier_Parametric_Scattering_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
SketchEdit: Mask-Free Local Image Manipulation With Partial Sketches | Yu Zeng, Zhe Lin, Vishal M. Patel | Sketch-based image manipulation is an interactive image editing task to modify an image based on input sketches from users. Existing methods typically convert this task into a conditional inpainting problem, which requires an additional mask from users indicating the region to modify. Then the masked regions are regarded as missing and filled by an inpainting model conditioned on the sketch. With this formulation, paired training data can be easily obtained by randomly creating masks and extracting edges or contours. Although this setup simplifies data preparation and model design, it complicates user interaction and discards useful information in masked regions. To this end, we propose a new framework for sketch-based image manipulation that only requires sketch inputs from users and utilizes the entire original image. Given an image and sketch, our model automatically predicts the target modification region and encodes it into a structure agnostic style vector. A generator then synthesizes the new image content based on the style vector and sketch. The manipulated image is finally produced by blending the generator output into the modification region of the original image. Our model can be trained in a self-supervised fashion by learning the reconstruction of an image region from the style vector and sketch. The proposed framework offers simpler and more intuitive user workflows for sketch-based image manipulation and provides better results than previous approaches. The code and interactive demo can be found in the supplementary material. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_SketchEdit_Mask-Free_Local_Image_Manipulation_With_Partial_Sketches_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zeng_SketchEdit_Mask-Free_Local_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_SketchEdit_Mask-Free_Local_Image_Manipulation_With_Partial_Sketches_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_SketchEdit_Mask-Free_Local_Image_Manipulation_With_Partial_Sketches_CVPR_2022_paper.html | CVPR 2022 | null |
ScaleNet: A Shallow Architecture for Scale Estimation | Axel Barroso-Laguna, Yurun Tian, Krystian Mikolajczyk | In this paper, we address the problem of estimating scale factors between images. We formulate the scale estimation problem as a prediction of a probability distribution over scale factors. We design a new architecture, ScaleNet, that exploits dilated convolutions as well as self- and cross-correlation layers to predict the scale between images. We demonstrate that rectifying images with estimated scales leads to significant performance improvements for various tasks and methods. Specifically, we show how ScaleNet can be combined with sparse local features and dense correspondence networks to improve camera pose estimation, 3D reconstruction, or dense geometric matching in different benchmarks and datasets. We provide an extensive evaluation on several tasks, and analyze the computational overhead of ScaleNet. The code, evaluation protocols, and trained models are publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Barroso-Laguna_ScaleNet_A_Shallow_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
E2EC: An End-to-End Contour-Based Method for High-Quality High-Speed Instance Segmentation | Tao Zhang, Shiqing Wei, Shunping Ji | Contour-based instance segmentation methods have developed rapidly recently but feature rough and handcrafted front-end contour initialization, which restricts the model performance, and an empirical and fixed backend predicted-label vertex pairing, which contributes to the learning difficulty. In this paper, we introduce a novel contour-based method, named E2EC, for high-quality instance segmentation. Firstly, E2EC applies a novel learnable contour initialization architecture instead of handcrafted contour initialization. This consists of a contour initialization module for constructing more explicit learning goals and a global contour deformation module for taking advantage of all of the vertices' features better. Secondly, we propose a novel label sampling scheme, named multi-direction alignment, to reduce the learning difficulty. Thirdly, to improve the quality of the boundary details, we dynamically match the most appropriate predicted-ground truth vertex pairs and propose the corresponding loss function named dynamic matching loss. The experiments showed that E2EC can achieve a state-of-the-art performance on the KITTI INStance (KINS) dataset, the Semantic Boundaries Dataset (SBD), the Cityscapes and the COCO dataset. E2EC is also efficient for use in real-time applications, with an inference speed of 36 fps for 512x512 images on an NVIDIA A6000 GPU. Code will be released at https://github.com/zhang-tao-whu/e2ec. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_E2EC_An_End-to-End_Contour-Based_Method_for_High-Quality_High-Speed_Instance_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.04074 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_E2EC_An_End-to-End_Contour-Based_Method_for_High-Quality_High-Speed_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_E2EC_An_End-to-End_Contour-Based_Method_for_High-Quality_High-Speed_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Bounded Adversarial Attack on Deep Content Features | Qiuling Xu, Guanhong Tao, Xiangyu Zhang | We propose a novel adversarial attack targeting content features in some deep layer, that is, individual neurons in the layer. A naive method that enforces a fixed value/percentage bound for neuron activation values can hardly work and generates very noisy samples. The reason is that the level of perceptual variation entailed by a fixed value bound is non-uniform across neurons and even for the same neuron. We hence propose a novel distribution quantile bound for activation values and a polynomial barrier loss function. Given a benign input, a fixed quantile bound is translated to many value bounds, one for each neuron, based on the distributions of the neuron's activations and the current activation value on the given input. These individualized bounds enable fine-grained regulation, allowing content feature mutations with bounded perceptional variations. Our evaluation on ImageNet and five different model architectures demonstrates that our attack is effective. Compared to seven other latest adversarial attacks in both the pixel space and the feature space, our attack can achieve the state-of-the-art trade-off between attack success rate and imperceptibility. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Bounded_Adversarial_Attack_on_Deep_Content_Features_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Bounded_Adversarial_Attack_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Bounded_Adversarial_Attack_on_Deep_Content_Features_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Bounded_Adversarial_Attack_on_Deep_Content_Features_CVPR_2022_paper.html | CVPR 2022 | null |
BatchFormer: Learning To Explore Sample Relationships for Robust Representation Learning | Zhi Hou, Baosheng Yu, Dacheng Tao | Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in a vanilla way (i.e., from the perspectives of either the input or the loss function), failing to explore the internal structure of deep neural networks for learning with sample relationships. Inspired by this, we propose to enable deep neural networks themselves with the ability to learn the sample relationships from each mini-batch. Specifically, we introduce a batch transformer module or BatchFormer, which is then applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. By doing this, the proposed method enables the collaboration of different samples, e.g., the head-class samples can also contribute to the learning of the tail classes for long-tailed recognition. Furthermore, to mitigate the gap between training and testing, we share the classifier between with or without the BatchFormer during training, which can thus be removed during testing. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning. Code is made publicly available at https://github.com/zhihou7/BatchFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hou_BatchFormer_Learning_To_Explore_Sample_Relationships_for_Robust_Representation_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hou_BatchFormer_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01522 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_BatchFormer_Learning_To_Explore_Sample_Relationships_for_Robust_Representation_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_BatchFormer_Learning_To_Explore_Sample_Relationships_for_Robust_Representation_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Image-Specific Prototype Exploration for Weakly Supervised Semantic Segmentation | Qi Chen, Lingxiao Yang, Jian-Huang Lai, Xiaohua Xie | Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has attracted much attention due to low annotation costs. Existing methods often rely on Class Activation Mapping (CAM) that measures the correlation between image pixels and classifier weight. However, the classifier focuses only on the discriminative regions while ignoring other useful information in each image, resulting in incomplete localization maps. To address this issue, we propose a Self-supervised Image-specific Prototype Exploration (SIPE) that consists of an Image-specific Prototype Exploration (IPE) and a General-Specific Consistency (GSC) loss. Specifically, IPE tailors prototypes for every image to capture complete regions, formed our Image-Specific CAM (IS-CAM). GSC is proposed to construct the consistency of general CAM and our specific IS-CAM, which further optimizes the feature representation and empowers a self-correction ability of prototype exploration. Extensive experiments are conducted on PASCAL VOC 2012 and MS COCO 2014 segmentation benchmark and results show our SIPE achieves new state-of-the-art performance using only image-level labels. The code is available at https://github.com/chenqi1126/SIPE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Self-Supervised_Image-Specific_Prototype_Exploration_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Self-Supervised_Image-Specific_Prototype_Exploration_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Self-Supervised_Image-Specific_Prototype_Exploration_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification | Philip Chikontwe, Soopil Kim, Sang Hyun Park | Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning. Other methods leverage spatial features to learn pixel-level correspondence while jointly training a classifier. However, results using such approaches show marginal improvements. In this paper, inspired by the transformer style self-attention mechanism, we propose a strategy to cross-attend and re-weight discriminative features for few-shot classification. Given a base representation of support and query images after global pooling, we introduce a single shared module that projects features and cross-attends in two aspects: (i) query to support, and (ii) support to query. The module computes attention scores between features to produce an attention pooled representation of features in the same class that is later added to the original representation followed by a projection head. This effectively re-weights features in both aspects (i & ii) to produce features that better facilitate improved metric-based meta-learning. Extensive experiments on public benchmarks show our approach outperforms state-of-the-art methods by 3% 5%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chikontwe_CAD_Co-Adapting_Discriminative_Features_for_Improved_Few-Shot_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chikontwe_CAD_Co-Adapting_Discriminative_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13465 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chikontwe_CAD_Co-Adapting_Discriminative_Features_for_Improved_Few-Shot_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chikontwe_CAD_Co-Adapting_Discriminative_Features_for_Improved_Few-Shot_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations | Zirui Peng, Shaofeng Li, Guoxing Chen, Cheng Zhang, Haojin Zhu, Minhui Xue | In this paper, we propose a novel and practical mechanism which enables the service provider to verify whether a suspect model is stolen from the victim model via model extraction attacks. Our key insight is that the profile of a DNN model's decision boundary can be uniquely characterized by its Universal Adversarial Perturbations (UAPs). UAPs belong to a low-dimensional subspace and piracy models' subspaces are more consistent with victim model's subspace compared with non-piracy model. Based on this, we propose a UAP fingerprinting method for DNN models and train an encoder via contrastive learning that takes fingerprint as inputs, outputs a similarity score. Extensive studies show that our framework can detect model IP breaches with confidence > 99.99% within only 20 fingerprints of the suspect model. It has good generalizability across different model architectures and is robust against post-modifications on stolen models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_Fingerprinting_Deep_Neural_Networks_Globally_via_Universal_Adversarial_Perturbations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_Fingerprinting_Deep_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.08602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Fingerprinting_Deep_Neural_Networks_Globally_via_Universal_Adversarial_Perturbations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Fingerprinting_Deep_Neural_Networks_Globally_via_Universal_Adversarial_Perturbations_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Multi-View Aggregation in the Wild for Large-Scale 3D Semantic Segmentation | Damien Robert, Bruno Vallet, Loic Landrieu | Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points. Merging large-scale point clouds and images raises several challenges, such as constructing a mapping between points and pixels, and aggregating features between multiple views. Current methods require mesh reconstruction or specialized sensors to recover occlusions, and use heuristics to select and aggregate available images. In contrast, we propose an end-to-end trainable multi-view aggregation model leveraging the viewing conditions of 3D points to merge features from images taken at arbitrary positions. Our method can combine standard 2D and 3D networks and outperforms both 3D models operating on colorized point clouds and hybrid 2D/3D networks without requiring colorization, meshing, or true depth maps. We set a new state-of-the-art for large-scale indoor/outdoor semantic segmentation on S3DIS (74.7 mIoU 6-Fold) and on KITTI-360 (58.3 mIoU). Our full pipeline is accessible at https://github.com/drprojects/DeepViewAgg, and only requires raw 3D scans and a set of images and poses. | https://openaccess.thecvf.com/content/CVPR2022/papers/Robert_Learning_Multi-View_Aggregation_in_the_Wild_for_Large-Scale_3D_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Robert_Learning_Multi-View_Aggregation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07548 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Robert_Learning_Multi-View_Aggregation_in_the_Wild_for_Large-Scale_3D_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Robert_Learning_Multi-View_Aggregation_in_the_Wild_for_Large-Scale_3D_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-Wise Semantic Alignment and Generation | Jianan Wang, Guansong Lu, Hang Xu, Zhenguo Li, Chunjing Xu, Yanwei Fu | Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical application. In this work, we study a novel task on text-guided image manipulation on the entity level in the real world. The task imposes three basic requirements, (1) to edit the entity consistent with the text descriptions, (2) to preserve the text-irrelevant regions, and (3) to merge the manipulated entity into the image naturally. To this end, we propose a new transformer-based framework based on the two-stage image synthesis method, namely ManiTrans, which can not only edit the appearance of entities but also generate new entities corresponding to the text guidance. Our framework incorporates a semantic alignment module to locate the image regions to be manipulated, and a semantic loss to help align the relationship between the vision and language. We conduct extensive experiments on the real datasets, CUB, Oxford, and COCO datasets to verify that our method can distinguish the relevant and irrelevant regions and achieve more precise and flexible manipulation compared with baseline methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ManiTrans_Entity-Level_Text-Guided_Image_Manipulation_via_Token-Wise_Semantic_Alignment_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_ManiTrans_Entity-Level_Text-Guided_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04428 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ManiTrans_Entity-Level_Text-Guided_Image_Manipulation_via_Token-Wise_Semantic_Alignment_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ManiTrans_Entity-Level_Text-Guided_Image_Manipulation_via_Token-Wise_Semantic_Alignment_and_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Video Model Transfer With Dynamic Representation Learning | Yi Li, Nuno Vasconcelos | Temporal modeling is an essential element in video understanding. While deep convolution-based architectures have been successful at solving large-scale video recognition datasets, recent work has pointed out that they are biased towards modeling short-range relations, often failing to capture long-term temporal structures in the videos, leading to poor transfer and generalization to new datasets. In this work, the problem of dynamic representation learning (DRL) is studied. We propose dynamic score, a measure of video dynamic modeling that describes the additional amount of information learned by a video network that cannot be captured by pure spatial student through knowledge distillation. DRL is then formulated as an adversarial learning problem between the video and spatial models, with the objective of maximizing the dynamic score of learned spatiotemporal classifier. The quality of learned video representations are evaluated on a diverse set of transfer learning problems concerning many-shot and few-shot action classification. Experimental results show that models learned with DRL outperform baselines in dynamic modeling, demonstrating higher transferability and generalization capacity to novel domains and tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Improving_Video_Model_Transfer_With_Dynamic_Representation_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Improving_Video_Model_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Improving_Video_Model_Transfer_With_Dynamic_Representation_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Improving_Video_Model_Transfer_With_Dynamic_Representation_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition | Partha Das, Sezer Karaoglu, Theo Gevers | Intrinsic image decomposition is the process of recovering the image formation components (reflectance and shading) from an image. Previous methods employ either explicit priors to constrain the problem or implicit constraints as formulated by their losses (deep learning). These methods can be negatively influenced by strong illumination conditions causing shading-reflectance leakages. Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is proposed for intrinsic image decomposition. Edges correspond to illumination invariant gradients. To handle hard negative illumination transitions, a hierarchical approach is taken including global and local refinement layers. We make use of attention layers to further strengthen the learning process. An extensive ablation study and large scale experiments are conducted showing that it is beneficial for edge-driven hybrid IID networks to make use of illumination invariant descriptors and that separating global and local cues helps in improving the performance of the network. Finally, it is shown that the proposed method obtains state of the art performance and is able to generalise well to real world images. The project page with pretrained models, finetuned models and network code can be found at: https://ivi.fnwi.uva.nl/cv/pienet/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Das_PIE-Net_Photometric_Invariant_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.html | CVPR 2022 | null |
Clothes-Changing Person Re-Identification With RGB Modality Only | Xinqian Gu, Hong Chang, Bingpeng Ma, Shutao Bai, Shiguang Shan, Xilin Chen | The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multi-modality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at: https://github.com/guxinqian/Simple-CCReID. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Clothes-Changing_Person_Re-Identification_With_RGB_Modality_Only_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_Clothes-Changing_Person_Re-Identification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Clothes-Changing_Person_Re-Identification_With_RGB_Modality_Only_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Clothes-Changing_Person_Re-Identification_With_RGB_Modality_Only_CVPR_2022_paper.html | CVPR 2022 | null |
Chitransformer: Towards Reliable Stereo From Cues | Qing Su, Shihao Ji | Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenges and can achieve satisfactory results with the extracted monocular cues, the lack of stereoscopic relationship renders the monocular prediction less reliable on its own, especially in highly dynamic or cluttered environments. To address these issues in both scenarios, we present an optic-chiasm-inspired self-supervised binocular depth estimation method, wherein a vision transformer (ViT) with gated positional cross-attention (GPCA) layers is designed to enable feature-sensitive pattern retrieval between views while retaining the extensive context information aggregated through self-attentions. Monocular cues from a single view are thereafter conditionally rectified by a blending layer with the retrieved pattern pairs. This crossover design is biologically analogous to the optic-chasma structure in the human visual system and hence the name, ChiTransformer. Our experiments show that this architecture yields substantial improvements over state-of-the-art self-supervised stereo approaches by 11%, and can be used on both rectilinear and non-rectilinear (e.g., fisheye) images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Su_Chitransformer_Towards_Reliable_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04554 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Image Forgery Detection Over Online Social Network Shared Images | Haiwei Wu, Jiantao Zhou, Jinyu Tian, Jun Liu | The increasing abuse of image editing softwares, such as Photoshop and Meitu, causes the authenticity of digital images questionable. Meanwhile, the widespread availability of online social networks (OSNs) makes them the dominant channels for transmitting forged images to report fake news, propagate rumors, etc. Unfortunately, various lossy operations adopted by OSNs, e.g., compression and resizing, impose great challenges for implementing the robust image forgery detection. To fight against the OSN-shared forgeries, in this work, a novel robust training scheme is proposed. We first conduct a thorough analysis of the noise introduced by OSNs, and decouple it into two parts, i.e., predictable noise and unseen noise, which are modelled separately. The former simulates the noise introduced by the disclosed (known) operations of OSNs, while the latter is designed to not only complete the previous one, but also take into account the defects of the detector itself. We then incorporate the modelled noise into a robust training framework, significantly improving the robustness of the image forgery detector. Extensive experimental results are presented to validate the superiority of the proposed scheme compared with several state-of-the-art competitors. Finally, to promote the future development of the image forgery detection, we build a public forgeries dataset based on four existing datasets and three most popular OSNs. The designed detector recently won the top ranking in a certificate forgery detection competition. The source code and dataset are available at https://github.com/HighwayWu/ImageForensicsOSN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Robust_Image_Forgery_Detection_Over_Online_Social_Network_Shared_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Robust_Image_Forgery_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Robust_Image_Forgery_Detection_Over_Online_Social_Network_Shared_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Robust_Image_Forgery_Detection_Over_Online_Social_Network_Shared_Images_CVPR_2022_paper.html | CVPR 2022 | null |
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation | Xueqi Hu, Xinyue Zhou, Qiusheng Huang, Zhengyi Shi, Li Sun, Qingli Li | Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical for the generator to keep the source content and prevent it from unnecessary modifications. The self-supervised contrastive learning has already been successfully applied in the I2I. By constraining features from the same location to be closer than those from different ones, it implicitly ensures the result to take content from the source. However, previous work uses the features from random locations to impose the constraint, which may not be appropriate since some locations contain less information of source domain. Moreover, the feature itself does not reflect the relation with others. This paper deals with these problems by intentionally selecting significant anchor points for contrastive learning. We design a query-selected attention (QS-Attn) module, which compares feature distances in the source domain, giving an attention matrix with a probability distribution in each row. Then we select queries according to their measurement of significance, computed from the distribution. The selected ones are regarded as anchors for contrastive loss. At the same time, the reduced attention matrix is employed to route features in both domains, so that source relations maintain in the synthesis. We validate our proposed method in three different I2I datasets, showing that it increases the image quality without adding learnable parameters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_QS-Attn_Query-Selected_Attention_for_Contrastive_Learning_in_I2I_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_QS-Attn_Query-Selected_Attention_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_QS-Attn_Query-Selected_Attention_for_Contrastive_Learning_in_I2I_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_QS-Attn_Query-Selected_Attention_for_Contrastive_Learning_in_I2I_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Physically Disentangled Intra- and Inter-Domain Adaptation for Varicolored Haze Removal | Yi Li, Yi Chang, Yan Gao, Changfeng Yu, Luxin Yan | Learning-based image dehazing methods have achieved marvelous progress during the past few years. On one hand, most approaches heavily rely on synthetic data and may face difficulties to generalize well in real scenes, due to the huge domain gap between synthetic and real images. On the other hand, very few works have considered the varicolored haze, caused by chromatic casts in real scenes. In this work, our goal is to handle the new task: real-world varicolored haze removal. To this end, we propose a physically disentangled joint intra- and inter-domain adaptation paradigm, in which intra-domain adaptation focuses on color correction and inter-domain procedure transfers knowledge between synthetic and real domains. We first learn to physically disentangle haze images into three components complying with the scattering model: background, transmission map, and atmospheric light. Since haze color is determined by atmospheric light, we perform intra-domain adaptation by specifically translating atmospheric light from varicolored space to unified color-balanced space, and then reconstructing color-balanced haze image through the scattering model. Consequently, we perform inter-domain adaptation between the synthetic and real images by mutually exchanging the background and other two components. Then we can reconstruct both identity and domain-translated haze images with self-consistency and adversarial loss. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art for real varicolored image dehazing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Physically_Disentangled_Intra-_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Physically_Disentangled_Intra-_and_Inter-Domain_Adaptation_for_Varicolored_Haze_Removal_CVPR_2022_paper.html | CVPR 2022 | null |
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection | Yu-Jhe Li, Jinhyung Park, Matthew O'Toole, Kris Kitani | Fusion of multiple sensor modalities such as camera, Lidar, and Radar, which are commonly found on autonomous vehicles, not only allows for accurate detection but also robustifies perception against adverse weather conditions and individual sensor failures. Due to inherent sensor characteristics, Radar performs well under extreme weather conditions (snow, rain, fog) that significantly degrade camera and Lidar. Recently, a few works have developed vehicle detection methods fusing Lidar and Radar signals, i.e., MVDNet. However, these models are typically developed under the assumption that the models always have access to two error-free sensor streams. If one of the sensors is unavailable or missing, the model may fail catastrophically. To mitigate this problem, we propose the Self-Training Multimodal Vehicle Detection Network (ST-MVDNet) which leverages a Teacher-Student mutual learning framework and a simulated sensor noise model used in strong data augmentation for Lidar and Radar. We show that by (1) enforcing output consistency between a Teacher network and a Student network and by (2) introducing missing modalities (strong augmentations) during training, our learned model breaks away from the error-free sensor assumption. This consistency enforcement enables the Student model to handle missing data properly and improve the Teacher model by updating it with the Student model's exponential moving average. Our experiments demonstrate that our proposed learning framework for multi-modal detection is able to better handle missing sensor data during inference. Furthermore, our method achieves new state-of-the-art performance (5% gain) on the Oxford Radar Robotcar dataset under various evaluation settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Modality-Agnostic_Learning_for_Radar-Lidar_Fusion_in_Vehicle_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty | Sihao Yu, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Zizhen Wang, Xueqi Cheng | Real-world data often exhibits class-imbalanced distributions, where a few classes (a.k.a. majority classes) occupy most instances and lots of classes (a.k.a. minority classes) have few instances. Neural classification models usually perform poorly on minority classes when training on such imbalanced datasets. To improve the performance on minority classes, existing methods typically re-balance the data distribution at the class level, i.e., assigning higher weights to minority classes and lower weights to majority classes during the training process. However, we observe that even the majority classes contain difficult instances to learn. By reducing the weights of the majority classes, such instances would become more difficult to learn and hurt the overall performance consequently. To tackle this problem, we propose a novel instance-level re-balancing strategy, which dynamically adjusts the sampling probabilities of instances according to the instance difficulty. Here the instance difficulty is measured based on the learning speed of instance, which is inspired by the human-leaning process (i.e., easier instances will be learned faster). We theoretically prove the correctness and convergence of our re-sampling algorithm. Empirical experiments demonstrate that our method significantly outperforms state-of-the-art re-balancing methods on the class-imbalanced datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_A_Re-Balancing_Strategy_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.html | CVPR 2022 | null |
Representation Compensation Networks for Continual Semantic Segmentation | Chang-Bin Zhang, Jia-Wen Xiao, Xialei Liu, Ying-Cong Chen, Ming-Ming Cheng | In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting. We propose to use a structural re-parameterization mechanism, named representation compensation (RC) module, to decouple the representation learning of both old and new knowledge. The RC module consists of two dynamically evolved branches with one frozen and one trainable. Besides, we design a pooled cube knowledge distillation strategy on both spatial and channel dimensions to further enhance the plasticity and stability of the model. We conduct experiments on two challenging continual semantic segmentation scenarios, continual class segmentation and continual domain segmentation. Without any extra computational overhead and parameters during inference, our method outperforms state-of-the-art performance. The code is available at https://github.com/zhangchbin/RCIL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Representation_Compensation_Networks_for_Continual_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Representation_Compensation_Networks_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Representation_Compensation_Networks_for_Continual_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Representation_Compensation_Networks_for_Continual_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Adaptive Gating for Single-Photon 3D Imaging | Ryan Po, Adithya Pediredla, Ioannis Gkioulekas | Single-photon avalanche diodes (SPADs) are growing in popularity for depth sensing tasks. However, SPADs still struggle in the presence of high ambient light due to the effects of pile-up. Conventional techniques leverage fixed or asynchronous gating to minimize pile-up effects, but these gating schemes are all non-adaptive, as they are unable to incorporate factors such as scene priors and previous photon detections into their gating strategy. We propose an adaptive gating scheme built upon Thompson sampling. Adaptive gating periodically updates the gate position based on prior photon observations in order to minimize depth errors. Our experiments show that our gating strategy results in significantly reduced depth reconstruction error and acquisition time, even when operating outdoors under strong sunlight conditions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Po_Adaptive_Gating_for_Single-Photon_3D_Imaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Po_Adaptive_Gating_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Po_Adaptive_Gating_for_Single-Photon_3D_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Po_Adaptive_Gating_for_Single-Photon_3D_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
Tracking People by Predicting 3D Appearance, Location and Pose | Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Jitendra Malik | We present an approach for tracking people in monocular videos by predicting their future 3D representations. To achieve this, we first lift people to 3D from a single frame in a robust manner. This lifting includes information about the 3D pose of the person, their location in the 3D space, and the 3D appearance. As we track a person, we collect 3D observations over time in a tracklet representation. Given the 3D nature of our observations, we build temporal models for each one of the previous attributes. We use these models to predict the future state of the tracklet, including 3D appearance, 3D location, and 3D pose. For a future frame, we compute the similarity between the predicted state of a tracklet and the single frame observations in a probabilistic manner. Association is solved with simple Hungarian matching, and the matches are used to update the respective tracklets. We evaluate our approach on various benchmarks and report state-of-the-art results. Code and models are available at: https://brjathu.github.io/PHALP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rajasegaran_Tracking_People_by_Predicting_3D_Appearance_Location_and_Pose_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rajasegaran_Tracking_People_by_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rajasegaran_Tracking_People_by_Predicting_3D_Appearance_Location_and_Pose_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rajasegaran_Tracking_People_by_Predicting_3D_Appearance_Location_and_Pose_CVPR_2022_paper.html | CVPR 2022 | null |
Text2Mesh: Text-Driven Neural Stylization for Meshes | Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka | In this work, we develop intuitive controls for editing the style of 3D objects. Our framework, Text2Mesh, stylizes a 3D mesh by predicting color and local geometric details which conform to a target text prompt. We consider a disentangled representation of a 3D object using a fixed mesh input (content) coupled with a learned neural network, which we term a neural style field network (NSF). In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP. Text2Mesh requires neither a pre-trained generative model nor a specialized 3D mesh dataset. It can handle low-quality meshes (non-manifold, boundaries, etc.) with arbitrary genus, and does not require UV parameterization. We demonstrate the ability of our technique to synthesize a myriad of styles over a wide variety of 3D meshes. Our code and results are available in our project webpage: https://threedle.github.io/text2mesh/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Michel_Text2Mesh_Text-Driven_Neural_Stylization_for_Meshes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Michel_Text2Mesh_Text-Driven_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Michel_Text2Mesh_Text-Driven_Neural_Stylization_for_Meshes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Michel_Text2Mesh_Text-Driven_Neural_Stylization_for_Meshes_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Solve Hard Minimal Problems | Petr Hruby, Timothy Duff, Anton Leykin, Tomas Pajdla | We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 microseconds. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two views. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hruby_Learning_To_Solve_Hard_Minimal_Problems_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hruby_Learning_To_Solve_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hruby_Learning_To_Solve_Hard_Minimal_Problems_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hruby_Learning_To_Solve_Hard_Minimal_Problems_CVPR_2022_paper.html | CVPR 2022 | null |
H4D: Human 4D Modeling by Learning Neural Compositional Representation | Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu | Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel framework that can effectively learn a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model. Particularly, our representation, named H4D, represents a dynamic 3D human over a temporal span with the SMPL parameters of shape and initial pose, and latent codes encoding motion and auxiliary information. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary code. Technically, we introduce novel GRU-based architectures to facilitate learning and improve the representation capability. Extensive experiments demonstrate our method is not only efficacy in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including motion retargeting, motion completion and future prediction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_H4D_Human_4D_Modeling_by_Learning_Neural_Compositional_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_H4D_Human_4D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_H4D_Human_4D_Modeling_by_Learning_Neural_Compositional_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_H4D_Human_4D_Modeling_by_Learning_Neural_Compositional_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
FWD: Real-Time Novel View Synthesis With Forward Warping and Depth | Ang Cao, Chris Rockwell, Justin Johnson | Novel view synthesis (NVS) is a challenging task requiring systems to generate photorealistic images of scenes from new viewpoints, where both quality and speed are important for applications. Previous image-based rendering (IBR) methods are fast, but have poor quality when input views are sparse. Recent Neural Radiance Fields (NeRF) and generalizable variants give impressive results but are not real-time. In our paper, we propose a generalizable NVS method with sparse inputs, called \FWDds, which gives high-quality synthesis in real-time. With explicit depth and differentiable rendering, it achieves competitive results to the SOTA methods with 130-1000xspeedup and better perceptual quality. If available, we can seamlessly integrate sensor depth during either training or inference to improve image quality while retaining real-time speed. With the growing prevalence of depths sensors, we hope that methods making use of depth will become increasingly useful. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_FWD_Real-Time_Novel_View_Synthesis_With_Forward_Warping_and_Depth_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_FWD_Real-Time_Novel_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_FWD_Real-Time_Novel_View_Synthesis_With_Forward_Warping_and_Depth_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_FWD_Real-Time_Novel_View_Synthesis_With_Forward_Warping_and_Depth_CVPR_2022_paper.html | CVPR 2022 | null |
Non-Generative Generalized Zero-Shot Learning via Task-Correlated Disentanglement and Controllable Samples Synthesis | Yaogong Feng, Xiaowen Huang, Pengbo Yang, Jian Yu, Jitao Sang | Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero Shot Learning (GZSL) problem. Most models achieve competitive performance but still suffer from two problems: (1) Feature confounding, the overall representations confound task-correlated and task-independent features, and existing models disentangle them in a generative way, but they are unreasonable to synthesize reliable pseudo samples with limited samples; (2) Distribution uncertainty, that massive data is needed when existing models synthesize samples from the uncertain distribution, which causes poor performance in limited samples of seen classes. In this paper, we propose a non-generative model to address these problems correspondingly in two modules: (1) Task-correlated feature disentanglement, to exclude the task-correlated features from task-independent ones by adversarial learning of domain adaption towards reasonable synthesis; (2) Controllable pseudo sample synthesis, to synthesize edge-pseudo and center-pseudo samples with certain characteristics towards more diversity generated and intuitive transfer. In addation, to describe the new scene that is the limit seen class samples in the training process, we further formulate a new ZSL task named the 'Few-shot Seen class and Zero-shot Unseen class learning' (FSZU). Extensive experiments on four benchmarks verify that the proposed method is competitive in the GZSL and the FSZU tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Non-Generative_Generalized_Zero-Shot_Learning_via_Task-Correlated_Disentanglement_and_Controllable_Samples_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.05335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Non-Generative_Generalized_Zero-Shot_Learning_via_Task-Correlated_Disentanglement_and_Controllable_Samples_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Non-Generative_Generalized_Zero-Shot_Learning_via_Task-Correlated_Disentanglement_and_Controllable_Samples_CVPR_2022_paper.html | CVPR 2022 | null |
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image | Zhang Chen, Zhiqiang Tian, Jihua Zhu, Ce Li, Shaoyi Du | Recently, many excellent weakly supervised semantic segmentation (WSSS) works are proposed based on class activation mapping (CAM). However, there are few works that consider the characteristics of medical images. In this paper, we find that there are mainly two challenges of medical images in WSSS: i) the boundary of object foreground and background is not clear; ii) the co-occurrence phenomenon is very severe in training stage. We thus propose a Causal CAM (C-CAM) method to overcome the above challenges. Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain. The category-causality chain represents the image content (cause) affects the category (effect). The anatomy-causality chain represents the anatomical structure (cause) affects the organ segmentation (effect). Extensive experiments were conducted on three public medical image data sets. Our C-CAM generates the best pseudo masks with the DSC of 77.26%, 80.34% and 78.15% on ProMRI, ACDC and CHAOS compared with other CAM-like methods. The pseudo masks of C-CAM are further used to improve the segmentation performance for organ segmentation tasks. Our C-CAM achieves DSC of 83.83% on ProMRI and DSC of 87.54% on ACDC, which outperforms state-of-the-art WSSS methods. Our code is available at https://github.com/Tian-lab/C-CAM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_C-CAM_Causal_CAM_for_Weakly_Supervised_Semantic_Segmentation_on_Medical_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_C-CAM_Causal_CAM_for_Weakly_Supervised_Semantic_Segmentation_on_Medical_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_C-CAM_Causal_CAM_for_Weakly_Supervised_Semantic_Segmentation_on_Medical_CVPR_2022_paper.html | CVPR 2022 | null |
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection | Alexandros Haliassos, Rodrigo Mira, Stavros Petridis, Maja Pantic | One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression. In this paper, we examine whether we can tackle this issue by harnessing videos of real talking faces, which contain rich information on natural facial appearance and behaviour and are readily available in large quantities online. Our method, termed RealForensics, consists of two stages. First, we exploit the natural correspondence between the visual and auditory modalities in real videos to learn, in a self-supervised cross-modal manner, temporally dense video representations that capture factors such as facial movements, expression, and identity. Second, we use these learned representations as targets to be predicted by our forgery detector along with the usual binary forgery classification task; this encourages it to base its real/fake decision on said factors. We show that our method achieves state-of-the-art performance on cross-manipulation generalisation and robustness experiments, and examine the factors that contribute to its performance. Our results suggest that leveraging natural and unlabelled videos is a promising direction for the development of more robust face forgery detectors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Haliassos_Leveraging_Real_Talking_Faces_via_Self-Supervision_for_Robust_Forgery_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Haliassos_Leveraging_Real_Talking_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.07131 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Haliassos_Leveraging_Real_Talking_Faces_via_Self-Supervision_for_Robust_Forgery_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Haliassos_Leveraging_Real_Talking_Faces_via_Self-Supervision_for_Robust_Forgery_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Forward Compatible Few-Shot Class-Incremental Learning | Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan | Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new class instances are insufficient, which is called few-shot class-incremental learning (FSCIL). Current methods handle incremental learning retrospectively by making the updated model similar to the old one. By contrast, we suggest learning prospectively to prepare for future updates, and propose ForwArd Compatible Training (FACT) for FSCIL. Forward compatibility requires future new classes to be easily incorporated into the current model based on the current stage data, and we seek to realize it by reserving embedding space for future new classes. In detail, we assign virtual prototypes to squeeze the embedding of known classes and reserve for new ones. Besides, we forecast possible new classes and prepare for the updating process. The virtual prototypes allow the model to accept possible updates in the future, which act as proxies scattered among embedding space to build a stronger classifier during inference. FACT efficiently incorporates new classes with forward compatibility and meanwhile resists forgetting of old ones. Extensive experiments validate FACT's state-of-the-art performance. Code is available at: https://github.com/zhoudw-zdw/CVPR22-Fact | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Forward_Compatible_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Forward_Compatible_Few-Shot_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06953 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Forward_Compatible_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Forward_Compatible_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule | Miao Zhang, Shirui Pan, Xiaojun Chang, Steven Su, Jilin Hu, Gholamreza (Reza) Haffari, Bin Yang | Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more recent works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as the search proceeds. Rather than directly optimizing the architecture parameters, this paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. By leveraging the natural-gradient variational inference (NGVI), the architecture distribution can be easily optimized based on existing codebases without incurring more memory and computational consumption. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability. The experimental results on NAS benchmark datasets confirm the significant improvements the proposed framework can make. In addition, instead of simply applying the argmax on the learned parameters, we further leverage the recently-proposed training-free proxies in NAS to select the optimal architecture from a group architectures drawn from the optimized distribution, where we achieve state-of-the-art results on the NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the DARTS search space also obtains competitive test errors with 2.37%, 15.72%, and 24.2% on CIFAR-10, CIFAR-100, and ImageNet, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_BaLeNAS_Differentiable_Architecture_Search_via_the_Bayesian_Learning_Rule_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_BaLeNAS_Differentiable_Architecture_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_BaLeNAS_Differentiable_Architecture_Search_via_the_Bayesian_Learning_Rule_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_BaLeNAS_Differentiable_Architecture_Search_via_the_Bayesian_Learning_Rule_CVPR_2022_paper.html | CVPR 2022 | null |
Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints To Better Classify Objects in Videos | Sukjun Hwang, Miran Heo, Seoung Wug Oh, Seon Joo Kim | Recently, both long-tailed recognition and object tracking have made great advances individually. TAO benchmark presented a mixture of the two, long-tailed object tracking, in order to further reflect the aspect of the real-world. To date, existing solutions have adopted detectors showing robustness in long-tailed distributions, which derive per-frame results. Then, they used tracking algorithms that combine the temporally independent detections to finalize tracklets. However, as the approaches did not take temporal changes in scenes into account, inconsistent classification results in videos led to low overall performance. In this paper, we present a set classifier that improves accuracy of classifying tracklets by aggregating information from multiple viewpoints contained in a tracklet. To cope with sparse annotations in videos, we further propose augmentation of tracklets that can maximize data efficiency. The set classifier is plug-and-playable to existing object trackers, and highly improves the performance of long-tailed object tracking. By simply attaching our method to QDTrack on top of ResNet-101, we achieve the new state-of-the-art, 19.9% and 15.7% TrackAP50 on TAO validation and test sets, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hwang_Cannot_See_the_Forest_for_the_Trees_Aggregating_Multiple_Viewpoints_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hwang_Cannot_See_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hwang_Cannot_See_the_Forest_for_the_Trees_Aggregating_Multiple_Viewpoints_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hwang_Cannot_See_the_Forest_for_the_Trees_Aggregating_Multiple_Viewpoints_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Canonical F-Correlation Projection for Compact Multiview Representation | Yun-Hao Yuan, Jin Li, Yun Li, Jipeng Qiang, Yi Zhu, Xiaobo Shen, Jianping Gou | Canonical correlation analysis (CCA) matters in multiview representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of covariance. In this paper, we address the preceding problem and propose a novel canonical F-correlation framework by exploring and exploiting the nonlinear relationship between different features. The framework projects each feature rather than observation into a certain new space by an arbitrary nonlinear mapping, thus resulting in more flexibility in real applications. With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping. Moreover, we further propose a multiset version of CCP dubbed MCCP for learning compact representation of more than two views. The proposed MCCP is solved by an iterative method, and we prove the convergence of this iteration. A series of experimental results on six benchmark datasets demonstrate the effectiveness of our proposed CCP and MCCP methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Learning_Canonical_F-Correlation_Projection_for_Compact_Multiview_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_Learning_Canonical_F-Correlation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Learning_Canonical_F-Correlation_Projection_for_Compact_Multiview_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Learning_Canonical_F-Correlation_Projection_for_Compact_Multiview_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
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