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Memory-Augmented Non-Local Attention for Video Super-Resolution | Jiyang Yu, Jingen Liu, Liefeng Bo, Tao Mei | In this paper, we propose a simple yet effective video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods predominantly leverage temporal neighbor frames to assist the super-resolution of the current frame. Those methods achieve limited performance as they suffer from the challenges in spatial frame alignment and the lack of useful information from similar LR neighbor frames. In contrast, we devise a cross-frame non-local attention mechanism that allows video super-resolution without frame alignment, leading to being more robust to large motions in the video. In addition, to acquire general video prior information beyond neighbor frames, and to compensate for the information loss caused by large motions, we design a novel memory-augmented attention module to memorize general video details during the super-resolution training. We have thoroughly evaluated our work on various challenging datasets. Compared to other recent video super-resolution approaches, our method not only achieves significant performance gains on large motion videos but also shows better generalization. Our source code and the new Parkour benchmark dataset will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Memory-Augmented_Non-Local_Attention_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2108.11048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis | Yurui Ren, Xiaoqing Fan, Ge Li, Shan Liu, Thomas H. Li | We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference images. To achieve this goal, a neural texture extraction and distribution operation based on double attention is described. This operation first extracts semantic neural textures from reference feature maps. Then, it distributes the extracted neural textures according to the spatial distributions learned from target poses. Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities. The disentangled representation further enables explicit appearance control. Neural textures of different reference images can be fused to control the appearance of the interested areas. Experimental comparisons show the superiority of the proposed model. Code is available at https://github.com/RenYurui/Neural-Texture-Extraction-Distribution. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.06160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Classification-Then-Grounding: Reformulating Video Scene Graphs As Temporal Bipartite Graphs | Kaifeng Gao, Long Chen, Yulei Niu, Jian Shao, Jun Xiao | Today's VidSGG models are all proposal-based methods, i.e., they first generate numerous paired subject-object snippets as proposals, and then conduct predicate classification for each proposal. In this paper, we argue that this prevalent proposal-based framework has three inherent drawbacks: 1) The ground-truth predicate labels for proposals are partially correct. 2) They break the high-order relations among different predicate instances of a same subject-object pair. 3) VidSGG performance is upper-bounded by the quality of the proposals. To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks. Meanwhile, under this framework, we reformulate the video scene graphs as temporal bipartite graphs, where the entities and predicates are two types of nodes with time slots, and the edges denote different semantic roles between these nodes. This formulation takes full advantage of our new framework. Accordingly, we further propose a novel BIpartite Graph based SGG model: BIG. It consists of a classification stage and a grounding stage, where the former aims to classify the categories of all the nodes and the edges, and the latter tries to localize the temporal location of each relation instance. Extensive ablations on two VidSGG datasets have attested to the effectiveness of our framework and BIG. Code is available at https://github.com/Dawn-LX/VidSGG-BIG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_Classification-Then-Grounding_Reformulating_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
Transformer-Empowered Multi-Scale Contextual Matching and Aggregation for Multi-Contrast MRI Super-Resolution | Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, Jing Qin | Magnetic resonance imaging (MRI) can present multi-contrast images of the same anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared with SR reconstruction using a single-contrast, multi-contrast SR reconstruction is promising to yield SR images with higher quality by leveraging diverse yet complementary information embedded in different imaging modalities. However, existing methods still have two shortcomings: (1) they neglect that the multi-contrast features at different scales contain different anatomical details and hence lack effective mechanisms to match and fuse these features for better reconstruction; and (2) they are still deficient in capturing long-range dependencies, which are essential for the regions with complicated anatomical structures. We propose a novel network to comprehensively address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques; we call it McMRSR. Firstly, we tame transformers to model long-range dependencies in both reference and target images. Then, a new multi-scale contextual matching method is proposed to capture corresponding contexts from reference features at different scales. Furthermore, we introduce a multi-scale aggregation mechanism to gradually and interactively aggregate multi-scale matched features for reconstructing the target SR MR image. Extensive experiments demonstrate that our network outperforms state-of-the-art approaches and has great potential to be applied in clinical practice. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Transformer-Empowered_Multi-Scale_Contextual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13963 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
GazeOnce: Real-Time Multi-Person Gaze Estimation | Mingfang Zhang, Yunfei Liu, Feng Lu | Appearance-based gaze estimation aims to predict the 3D eye gaze direction from a single image. While recent deep learning-based approaches have demonstrated excellent performance, they usually assume one calibrated face in each input image and cannot output multi-person gaze in real time. However, simultaneous gaze estimation for multiple people in the wild is necessary for real-world applications. In this paper, we propose the first one-stage end-to-end gaze estimation method, GazeOnce, which is capable of simultaneously predicting gaze directions for multiple faces (>10) in an image. In addition, we design a sophisticated data generation pipeline and propose a new dataset, MPSGaze, which contains full images of multiple people with 3D gaze ground truth. Experimental results demonstrate that our unified framework not only offers a faster speed, but also provides a lower gaze estimation error compared with state-of-the-art methods. This technique can be useful in real-time applications with multiple users. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_GazeOnce_Real-Time_Multi-Person_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.09480 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
GateHUB: Gated History Unit With Background Suppression for Online Action Detection | Junwen Chen, Gaurav Mittal, Ye Yu, Yu Kong, Mei Chen | Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of the history that are more informative to the prediction of the current frame. We present GateHUB, Gated History Unit with Background Suppression, that comprises a novel position-guided gated cross-attention mechanism to enhance or suppress parts of the history as per how informative they are for current frame prediction. GateHUB further proposes Future-augmented History (FaH) to make history features more informative by using subsequently observed frames when available. In a single unified framework, GateHUB integrates the transformer's ability of long-range temporal modeling and the recurrent model's capacity to selectively encode relevant information. GateHUB also introduces a background suppression objective to further mitigate false positive background frames that closely resemble the action frames. Extensive validation on three benchmark datasets, THUMOS, TVSeries, and HDD, demonstrates that GateHUB significantly outperforms all existing methods and is also more efficient than the existing best work. Furthermore, a flow-free version of GateHUB is able to achieve higher or close accuracy at 2.8x higher frame rate compared to all existing methods that require both RGB and optical flow information for prediction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_GateHUB_Gated_History_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Font Generation by Learning Fine-Grained Local Styles | Licheng Tang, Yiyang Cai, Jiaming Liu, Zhibin Hong, Mingming Gong, Minhu Fan, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG pipeline considers characters in a standard font library as content glyphs and transfers them to a new target font by extracting style information from the reference glyphs. Most existing solutions explicitly disentangle content and style of reference glyphs globally or component-wisely. However, the style of glyphs mainly lies in the local details, i.e. the styles of radicals, components, and strokes together depict the style of a glyph. Therefore, even a single character can contain different styles distributed over spatial locations. In this paper, we propose a new font generation approach by learning 1) the fine-grained local styles from references, and 2) the spatial correspondence between the content and reference glyphs. Therefore each spatial location in the content glyph can be assigned with the right fine-grained style. To this end, we adopt cross-attention over the representation of the content glyphs as the queries and the representations of the reference glyphs as the keys and values. Instead of explicitly disentangling global or component-wise modeling, the cross attention mechanism can attend to the right local styles in the reference glyphs and aggregates the reference styles into a fine-grained style representation for the given content glyphs. The experiments show that the proposed method outperforms the state-of-the-art methods in FFG. In particular, the user studies also demonstrate the style consistency of our approach is significantly outperforms previous methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Few-Shot_Font_Generation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.09965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.html | CVPR 2022 | null |
Bridging Video-Text Retrieval With Multiple Choice Questions | Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, Ping Luo | Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the "questions" constructed by the text features via resorting to the video features. Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to build questions, with which the video encoder can be trained to capture more regional content and temporal dynamics. In the form of questions and answers, the semantic associations between local video-text features can be properly established. BridgeFormer is able to be removed for downstream retrieval, rendering an efficient and flexible model with only two encoders. Our method outperforms state-of-the-art methods on the popular text-to-video retrieval task in five datasets with different experimental setups (i.e., zero-shot and fine-tune), including HowTo100M (one million videos). We further conduct zero-shot action recognition, which can be cast as video-to-text retrieval, and our approach also significantly surpasses its counterparts. As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e.g., action recognition with linear evaluation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ge_Bridging_Video-Text_Retrieval_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04850 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.html | CVPR 2022 | null |
Depth-Aware Generative Adversarial Network for Talking Head Video Generation | Fa-Ting Hong, Longhao Zhang, Li Shen, Dan Xu | Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving video. Existing works for this task heavily rely on 2D representations (e.g. appearance and motion) learned from the input images. However, dense 3D facial geometry (e.g. pixel-wise depth) is extremely important for this task as it is particularly beneficial for us to essentially generate accurate 3D face structures and distinguish noisy information from the possibly cluttered background. Nevertheless, dense 3D geometry annotations are prohibitively costly for videos and are typically not available for this video generation task. In this paper, we introduce a self-supervised face-depth learning method to automatically recover dense 3D facial geometry (i.e. depth) from the face videos without the requirement of any expensive 3D annotation data. Based on the learned dense depth maps, we further propose to leverage them to estimate sparse facial keypoints that capture the critical movement of the human head. In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i.e. appearance and depth) attention to guide the generation of motion fields for warping source image representations. All these contributions compose a novel depth-aware generative adversarial network (DaGAN) for talking head generation. Extensive experiments conducted demonstrate that our proposed method can generate highly realistic faces, and achieve significant results on the unseen human faces. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Depth-Aware_Generative_Adversarial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Dual-Path Image Inpainting With Auxiliary GAN Inversion | Wentao Wang, Li Niu, Jianfu Zhang, Xue Yang, Liqing Zhang | Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but still fails to handle large missing area or complex semantics. Recently, GAN inversion based inpainting methods propose to leverage semantic information in pretrained generator (e.g., StyleGAN) to solve the above issues. Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator. However, inferring the latent code is either time-consuming or inaccurate. In this paper, we develop a dual-path inpainting network with inversion path and feed-forward path, in which inversion path provides auxiliary information to help feed-forward path. We also design a novel deformable fusion module to align the feature maps in two paths. Experiments on FFHQ and LSUN demonstrate that our method is effective in solving the aforementioned problems while producing more realistic results than state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Dual-Path_Image_Inpainting_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.html | CVPR 2022 | null |
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis | Ming Tao, Hao Tang, Fei Wu, Xiao-Yuan Jing, Bing-Kun Bao, Changsheng Xu | Synthesizing high-quality realistic images from text descriptions is a challenging task. Existing text-to-image Generative Adversarial Networks generally employ a stacked architecture as the backbone yet still remain three flaws. First, the stacked architecture introduces the entanglements between generators of different image scales. Second, existing studies prefer to apply and fix extra networks in adversarial learning for text-image semantic consistency, which limits the supervision capability of these networks. Third, the cross-modal attention-based text-image fusion that widely adopted by previous works is limited on several special image scales because of the computational cost. To these ends, we propose a simpler but more effective Deep Fusion Generative Adversarial Networks (DF-GAN). To be specific, we propose: (i) a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators, (ii) a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output, which enhances the text-image semantic consistency without introducing extra networks, (iii) a novel deep text-image fusion block, which deepens the fusion process to make a full fusion between text and visual features. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images and achieves better performance on widely used datasets. Code is available at https://github.com/tobran/DF-GAN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Generative Flows With Invertible Attentions | Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc Van Gool | Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introduces two types of invertible attention mechanisms, i.e., map-based and transformer-based attentions, for both unconditional and conditional generative flows. The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows. The masked scheme allows for invertible attention modules with tractable Jacobian determinants, enabling its seamless integration at any positions of the flow-based models. The proposed attention mechanisms lead to more efficient generative flows, due to their capability of modeling the long-term data dependencies. Evaluation on multiple image synthesis tasks shows that the proposed attention flows result in efficient models and compare favorably against the state-of-the-art unconditional and conditional generative flows. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sukthanker_Generative_Flows_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.03959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.html | CVPR 2022 | null |
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers | Yunhui Guo, Xudong Wang, Yubei Chen, Stella X. Yu | Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outperforming Euclidean neural networks (ENNs) on datasets with known semantic hierarchies. However, HNNs underperform ENNs on standard benchmarks without clear hierarchies, greatly restricting HNNs' applicability in practice. Our key insight is that HNNs' poorer general classification performance results from vanishing gradients during backpropagation, caused by their hybrid architecture connecting Euclidean features to a hyperbolic classifier. We propose an effective solution by simply clipping the Euclidean feature magnitude while training HNNs. Our experiments demonstrate that clipped HNNs become super-hyperbolic classifiers: They are not only consistently better than HNNs which already outperform ENNs on hierarchical data, but also on-par with ENNs on MNIST, CIFAR10, CIFAR100 and ImageNet benchmarks, with better adversarial robustness and out-of-distribution detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Clipped_Hyperbolic_Classifiers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.11472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.html | CVPR 2022 | null |
Estimating Fine-Grained Noise Model via Contrastive Learning | Yunhao Zou, Ying Fu | Image denoising has achieved unprecedented progress as great efforts have been made to exploit effective deep denoisers. To improve the denoising performance in real-world, two typical solutions are used in recent trends: devising better noise models for the synthesis of more realistic training data, and estimating noise level function to guide non-blind denoisers. In this work, we combine both noise modeling and estimation, and propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation. Specifically, our model learns a noise estimation model with fine-grained statistical noise model in a contrastive manner. Then, we use the estimated noise parameters to model camera-specific noise distribution, and synthesize realistic noisy training data. The most striking thing for our work is that by calibrating noise models of several sensors, our model can be extended to predict other cameras. In other words, we can estimate camera-specific noise models for unknown sensors with only testing images, without any laborious calibration frames or paired noisy/clean data. The proposed pipeline endows deep denoisers with competitive performances with state-of-the-art real noise modeling methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.01716 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
DiffPoseNet: Direct Differentiable Camera Pose Estimation | Chethan M. Parameshwara, Gokul Hari, Cornelia Fermüller, Nitin J. Sanket, Yiannis Aloimonos | Current deep neural network approaches for camera pose estimation rely on scene structure for 3D motion estimation, but this decreases the robustness and thereby makes cross-dataset generalization difficult. In contrast, classical approaches to structure from motion estimate 3D motion utilizing optical flow and then compute depth. Their accuracy, however, depends strongly on the quality of the optical flow. To avoid this issue, direct methods have been proposed, which separate 3D motion from depth estimation but compute 3D motion using only image gradients in the form of normal flow. In this paper, we introduce a network NFlowNet, for normal flow estimation which is used to enforce robust and direct constraints. In particular, normal flow is used to estimate relative camera pose based on the cheirality (depth positivity) constraint. We achieve this by formulating the optimization problem as a differentiable cheirality layer, which allows for end-to-end learning of camera pose. We perform extensive qualitative and quantitative evaluation of the proposed DiffPoseNet's sensitivity to noise and its generalization across datasets. We compare our approach to existing state-of-the-art methods on KITTI, TartanAir, and TUM-RGBD datasets | https://openaccess.thecvf.com/content/CVPR2022/papers/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
The Flag Median and FlagIRLS | Nathan Mankovich, Emily J. King, Chris Peterson, Michael Kirby | Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Grassmann manifold, one is led to consider the idea of a subspace prototype for a Grassmann-valued dataset. While a number of different subspace prototypes have been described, the calculation of some of these prototypes has proven to be computationally expensive while other prototypes are affected by outliers and produce highly imperfect clustering on noisy data. This work proposes a new subspace prototype, the flag median, and introduces the FlagIRLS algorithm for its calculation. We provide evidence that the flag median is robust to outliers and can be used effectively in algorithms like Linde-Buzo-Grey (LBG) to produce improved clusterings on Grassmannians. Numerical experiments include a synthetic dataset, the MNIST handwritten digits dataset, the Mind's Eye video dataset and the UCF YouTube action dataset. The flag median is compared the other leading algorithms for computing prototypes on the Grassmannian, namely, the l_2-median and to the flag mean. We find that using FlagIRLS to compute the flag median converges in 4 iterations on a synthetic dataset. We also see that Grassmannian LBG with a codebook size of 20 and using the flag median produces at least a 10% improvement in cluster purity over Grassmannian LBG using the flag mean or l_2-median on the Mind's Eye dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mankovich_The_Flag_Median_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.04437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.html | CVPR 2022 | null |
Implicit Feature Decoupling With Depthwise Quantization | Iordanis Fostiropoulos, Barry Boehm | Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where quantization is applied to a decomposed sub-tensor along the feature axis of weak statistical dependence. The feature decomposition leads to an exponential increase in representation capacity with a linear increase in memory and parameter cost. In addition, DQ can be directly applied to existing encoder-decoder frameworks without modification of the DNN architecture. We use DQ in the context of Hierarchical Auto-Encoders and train end-to-end on an image feature representation. We provide an analysis of the cross-correlation between spatial and channel features and propose a decomposition of the image feature representation along the channel axis. The improved performance of the depthwise operator is due to the increased representation capacity from implicit feature decoupling. We evaluate DQ on the likelihood estimation task, where it outperforms the previous state-of-the-art on CIFAR-10, ImageNet-32 and ImageNet-64. We progressively train with increasing image size a single hierarchical model that uses 69% fewer parameters and has faster convergence than the previous work. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fostiropoulos_Implicit_Feature_Decoupling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.html | CVPR 2022 | null |
Graph-Context Attention Networks for Size-Varied Deep Graph Matching | Zheheng Jiang, Hossein Rahmani, Plamen Angelov, Sue Black, Bryan M. Williams | Deep learning for graph matching has received growing interest and developed rapidly in the past decade. Although recent deep graph matching methods have shown excellent performance on matching between graphs of equal size in the computer vision area, the size-varied graph matching problem, where the number of keypoints in the images of the same category may vary due to occlusion, is still an open and challenging problem. To tackle this, we firstly propose to formulate the combinatorial problem of graph matching as an Integer Linear Programming (ILP) problem, which is more flexible and efficient to facilitate comparing graphs of varied sizes. A novel Graph-context Attention Network (GCAN), which jointly capture intrinsic graph structure and cross-graph information for improving the discrimination of node features, is then proposed and trained to resolve this ILP problem with node correspondence supervision. We further show that the proposed GCAN model is efficient to resolve the graph-level matching problem and is able to automatically learn node-to-node similarity via graph-level matching. The proposed approach is evaluated on three public keypoint-matching datasets and one graph-matching dataset for blood vessel patterns, with experimental results showing its superior performance over existing state-of-the-art algorithms on the keypoint and graph-level matching tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Graph-Context_Attention_Networks_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
FENeRF: Face Editing in Neural Radiance Fields | Jingxiang Sun, Xuan Wang, Yong Zhang, Xiaoyu Li, Qi Zhang, Yebin Liu, Jue Wang | Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we propose FENeRF, a 3D-aware generator that can produce view-consistent and locally-editable portrait images. Our method uses two decoupled latent codes to generate corresponding facial semantics and texture in a spatial-aligned 3D volume with shared geometry. Benefiting from such underlying 3D representation, FENeRF can jointly render the boundary-aligned image and semantic mask and use the semantic mask to edit the 3D volume via GAN inversion. We further show such 3D representation can be learned from widely available monocular image and semantic mask pairs. Moreover, we reveal that joint learning semantics and texture helps to generate finer geometry. Our experiments demonstrate that FENeRF outperforms state-of-the-art methods in various face editing tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.15490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
CoNeRF: Controllable Neural Radiance Fields | Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi | We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attributes as latent variables that are regressed by the neural network given the scene encoding. This leads to a few-shot learning framework, where attributes are discovered automatically by the framework when annotations are not provided. We apply our method to various scenes with different types of controllable attributes (e.g. expression control on human faces, or state control in the movement of inanimate objects). Overall, we demonstrate, to the best of our knowledge, for the first time novel view and novel attribute re-rendering of scenes from a single video. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kania_CoNeRF_Controllable_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Noise2NoiseFlow: Realistic Camera Noise Modeling Without Clean Images | Ali Maleky, Shayan Kousha, Michael S. Brown, Marcus A. Brubaker | Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaussian noise or the heteroscedastic Gaussian noise model (a.k.a., camera noise level function) are not sufficient to learn the complex behavior of the camera sensor noise. Recently, more complex learning-based models have been proposed that yield better results in noise synthesis and downstream tasks, such as denoising. However, their dependence on supervised data (i.e., paired clean images) is a limiting factor given the challenges in producing ground-truth images. This paper proposes a framework for training a noise model and a denoiser simultaneously while relying only on pairs of noisy images rather than noisy/clean paired image data. We apply this framework to the training of the Noise Flow architecture. The noise synthesis and density estimation results show that our framework outperforms previous signal-processing-based noise models and is on par with its supervised counterpart. The trained denoiser is also shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches. The results indicate that the joint training of a denoiser and a noise model yields significant improvements in the denoiser. | https://openaccess.thecvf.com/content/CVPR2022/papers/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Maleky_Noise2NoiseFlow_Realistic_Camera_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2206.01103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.html | CVPR 2022 | null |
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes | Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko | Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.02740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Remember Intentions: Retrospective-Memory-Based Trajectory Prediction | Chenxin Xu, Weibo Mao, Wenjun Zhang, Siheng Chen | To realize trajectory prediction, most previous methods adopt the parameter-based approach, which encodes all the seen past-future instance pairs into model parameters. However, in this way, the model parameters come from all seen instances, which means a huge amount of irrelevant seen instances might also involve in predicting the current situation, disturbing the performance. To provide a more explicit link between the current situation and the seen instances, we imitate the mechanism of retrospective memory in neuropsychology and propose MemoNet, an instance-based approach that predicts the movement intentions of agents by looking for similar scenarios in the training data. In MemoNet, we design a pair of memory banks to explicitly store representative instances in the training set, acting as prefrontal cortex in the neural system, and a trainable memory addresser to adaptively search a current situation with similar instances in the memory bank, acting like basal ganglia. During prediction, MemoNet recalls previous memory by using the memory addresser to index related instances in the memory bank. We further propose a two-step trajectory prediction system, where the first step is to leverage MemoNet to predict the destination and the second step is to fulfill the whole trajectory according to the predicted destinations. Experiments show that the proposed MemoNet improves the FDE by 20.3%/10.2%/28.3% from the previous best method on SDD/ETH-UCY/NBA datasets. Experiments also show that our MemoNet has the ability to trace back to specific instances during prediction, promoting more interpretability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Remember_Intentions_Retrospective-Memory-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11474 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Measuring Compositional Consistency for Video Question Answering | Mona Gandhi, Mustafa Omer Gul, Eva Prakash, Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala | Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is difficult to discern whether models arrive at answers using compositional reasoning or by leveraging data biases. In this paper, we develop a question decomposition engine that programmatically deconstructs a compositional question into a directed acyclic graph of sub-questions. The graph is designed such that each parent question is a composition of its children. We present AGQA-Decomp, a benchmark containing 2.3M question graphs, with an average of 11.49 sub-questions per graph, and 4.55M total new sub-questions. Using question graphs, we evaluate three state-of-the-art models with a suite of novel compositional consistency metrics. We find that models either cannot reason correctly through most compositions or are reliant on incorrect reasoning to reach answers, frequently contradicting themselves or achieving high accuracies when failing at intermediate reasoning steps. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gandhi_Measuring_Compositional_Consistency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07190 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks | Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao | Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks. By considering instance contrastive learning as a dictionary look-up operation, we construct a semantics-aware dictionary with samples from both source and target domains where each target sample is assigned a (pseudo) category label based on the category priors of source samples. This allows category contrastive learning (between target queries and the category-level dictionary) for category-discriminative yet domain-invariant feature representations: samples of the same category (from either source or target domain) are pulled closer while those of different categories are pushed apart simultaneously. Extensive UDA experiments in multiple visual tasks (e.g., segmentation, classification and detection) show that CaCo achieves superior performance as compared with state-of-the-art methods. The experiments also demonstrate that CaCo is complementary to existing UDA methods and generalizable to other learning setups such as unsupervised model adaptation, open-/partial-set adaptation etc. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Category_Contrast_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.02885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering | Vipul Gupta, Zhuowan Li, Adam Kortylewski, Chenyu Zhang, Yingwei Li, Alan Yuille | While Visual Question Answering (VQA) has progressed rapidly, previous works raise concerns about robustness of current VQA models. In this work, we study the robustness of VQA models from a novel perspective: visual context. We suggest that the models over-rely on the visual context, i.e., irrelevant objects in the image, to make predictions. To diagnose the models' reliance on visual context and measure their robustness, we propose a simple yet effective perturbation technique, SwapMix. SwapMix perturbs the visual context by swapping features of irrelevant context objects with features from other objects in the dataset. Using SwapMix we are able to change answers to more than 45% of the questions for a representative VQA model. Additionally, we train the models with perfect sight and find that the context over-reliance highly depends on the quality of visual representations. In addition to diagnosing, SwapMix can also be applied as a data augmentation strategy during training in order to regularize the context over-reliance. By swapping the context object features, the model reliance on context can be suppressed effectively. Two representative VQA models are studied using SwapMix: a co-attention model MCAN and a large-scale pretrained model LXMERT. Our experiments on the popular GQA dataset show the effectiveness of SwapMix for both diagnosing model robustness, and regularizing the over-reliance on visual context. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02285 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.html | CVPR 2022 | null |
UNIST: Unpaired Neural Implicit Shape Translation Network | Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang | We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of the art. Furthermore, our translation network is trained to perform the task over a latent grid representation which combines the merits of both latent-space processing and position awareness, to not only enable drastic shape transforms but also well preserve spatial features and fine local details for natural shape translations. With the same network architecture and only dictated by the input domain pairs, our model can learn both style-preserving content alteration and content-preserving style transfer. We demonstrate the generality and quality of the translation results, and compare them to well-known baselines. Code is available at https://qiminchen.github.io/unist/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_UNIST_Unpaired_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Local-Adaptive Face Recognition via Graph-Based Meta-Clustering and Regularized Adaptation | Wenbin Zhu, Chien-Yi Wang, Kuan-Lun Tseng, Shang-Hong Lai, Baoyuan Wang | Due to the rising concern of data privacy, it's reasonable to assume the local client data can't be transferred to a centralized server, nor their associated identity label is provided. To support continuous learning and fill the last-mile quality gap, we introduce a new problem setup called Local-Adaptive Face Recognition (LaFR). Leveraging the environment-specific local data after the deployment of the initial global model, LaFR aims at getting optimal performance by training local-adapted models automatically and un-supervisely, as opposed to fixing their initial global model. We achieve this by a newly proposed embedding cluster model based on Graph Convolution Network (GCN), which is trained via meta-optimization procedure. Compared with previous works, our meta-clustering model can generalize well in unseen local environments. With the pseudo identity labels from the clustering results, we further introduce novel regularization techniques to improve the model adaptation performance. Extensive experiments on racial and internal sensor adaptation demonstrate that our proposed solution is more effective for adapting face recognition models in each specific environment. Meanwhile, we show that LaFR can further improve the global model by a simple federated aggregation over the updated local models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Local-Adaptive_Face_Recognition_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14327 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
The DEVIL Is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting | Ryan Szeto, Jason J. Corso | Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differently, existing evaluation schemes fail to control for them, thereby providing minimal insight into inpainting failure modes. To address this gap, we propose the Diagnostic Evaluation of Video Inpainting on Landscapes (DEVIL) benchmark, which consists of two contributions: (i) a novel dataset of videos and masks labeled according to several key inpainting failure modes, and (ii) an evaluation scheme that samples slices of the dataset characterized by a fixed content attribute, and scores performance on each slice according to reconstruction, realism, and temporal consistency quality. By revealing systematic changes in performance induced by particular characteristics of the input content, our challenging benchmark enables more insightful analysis into video inpainting methods and serves as an invaluable diagnostic tool for the field. Our code and data are available at github.com/MichiganCOG/devil. | https://openaccess.thecvf.com/content/CVPR2022/papers/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Szeto_The_DEVIL_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.05332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Mutual Information-Driven Pan-Sharpening | Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, Feng Zhao | Pan-sharpening aims to integrate the complementary information of texture-rich PAN images and multi-spectral (MS) images to produce the texture-rich MS images. Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images. This leads to information redundancy not being handled well, which further limits the performance of these methods. To address the above issue, we propose a novel mutual information-driven Pan-sharpening framework in this paper. To be specific, we first project the PAN and MS image into modality-aware feature space independently, and then impose the mutual information minimization over them to explicitly encourage the complementary information learning. Such operation is capable of reducing the information redundancy and improving the model performance. Extensive experimental results over multiple satellite datasets demonstrate that the proposed algorithm outperforms other state-of-the-art methods qualitatively and quantitatively with great generalization ability to real-world scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.html | CVPR 2022 | null |
Shifting More Attention to Visual Backbone: Query-Modulated Refinement Networks for End-to-End Visual Grounding | Jiabo Ye, Junfeng Tian, Ming Yan, Xiaoshan Yang, Xuwu Wang, Ji Zhang, Liang He, Xin Lin | Visual grounding focuses on establishing fine-grained alignment between vision and natural language, which has essential applications in multimodal reasoning systems. Existing methods use pre-trained query-agnostic visual backbones to extract visual feature maps independently without considering the query information. We argue that the visual features extracted from the visual backbones and the features really needed for multimodal reasoning are inconsistent. One reason is that there are differences between pre-training tasks and visual grounding. Moreover, since the backbones are query-agnostic, it is difficult to completely avoid the inconsistency issue by training the visual backbone end-to-end in the visual grounding framework. In this paper, we propose a Query-modulated Refinement Network (QRNet) to address the inconsistent issue by adjusting intermediate features in the visual backbone with a novel Query-aware Dynamic Attention (QD-ATT) mechanism and query-aware multiscale fusion. The QD-ATT can dynamically compute query-dependent visual attention at the spatial and channel level of the feature maps produced by the visual backbone. We apply the QRNet to an end-to-end visual grounding framework. Extensive experiments show that the proposed method outperforms state-of-the-art methods on five widely used datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Shifting_More_Attention_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15442 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.html | CVPR 2022 | null |
A Framework for Learning Ante-Hoc Explainable Models via Concepts | Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, Vineeth N Balasubramanian | Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and jointly trains the whole module that shows high predictive performance and generates meaningful explanations in terms of concepts. Our training strategy is suitable for unsupervised concept learning with much lesser parameter space requirements compared to baseline methods. Our proposed model also has provision for leveraging self-supervision on concepts to extract better explanations. However, with full concept supervision, we achieve the best predictive performance compared to recently proposed concept-based explainable models. We report both qualitative and quantitative results with our method, which shows better performance than recently proposed concept-based explainability methods. We reported exhaustive results with two datasets without ground truth concepts, i.e., CIFAR10, ImageNet, and two datasets with ground truth concepts, i.e., AwA2, CUB-200, to show the effectiveness of our method for both cases. To the best of our knowledge, we are the first ante-hoc explanation generation method to show results with a large-scale dataset such as ImageNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sarkar_A_Framework_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.11761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.html | CVPR 2022 | null |
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior | Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or Litany | Evaluating and improving planning for autonomous vehicles requires scalable generation of long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging, but not impossible to drive through safely. In this work, we introduce STRIVE, a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. Scenario generation is formulated as an optimization in the latent space of this traffic model, perturbing an initial real-world scene to produce trajectories that collide with a given planner. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner. Further analysis clusters generated scenarios based on collision type. We attack two planners and show that STRIVE successfully generates realistic, challenging scenarios in both cases. We additionally "close the loop" and use these scenarios to optimize hyperparameters of a rule-based planner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rempe_Generating_Useful_Accident-Prone_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.05077 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
FLOAT: Factorized Learning of Object Attributes for Improved Multi-Object Multi-Part Scene Parsing | Rishubh Singh, Pranav Gupta, Pradeep Shenoy, Ravikiran Sarvadevabhatla | Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independent dense prediction of object category and part attributes which increases scalability and reduces task complexity compared to the monolithic label space counterpart. In addition, we propose an inference-time 'zoom' refinement technique which significantly improves segmentation quality, especially for smaller objects/parts. Compared to state of the art, FLOAT obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU. We incorporate previously excluded part attributes and other minor parts of the Pascal-Part dataset to create the most comprehensive and challenging version which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and 7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness across a challenging diversity of objects and parts. The code and datasets are available at floatseg.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16168 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Geometry-Aware 3D Generative Adversarial Networks | Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, Gordon Wetzstein | Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the generated images and the latter adversely affects multi-view consistency and shape quality. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, our framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN2, and inherit their efficiency and expressiveness. We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats, among other experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_Efficient_Geometry-Aware_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.07945 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks | Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li | In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the players' strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. For scalability of the framework where multiple generators and discriminator best responses are stored in the memory, we propose two solutions: 1) pruning the weakly-dominated players' strategies to keep the oracles from becoming intractable; 2) applying continual learning to retain the previous knowledge of the networks. We apply our framework to established GAN architectures such as vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked GAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and show that DO-GAN variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective GAN architectures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Aung_DO-GAN_A_Double_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Dancing Under the Stars: Video Denoising in Starlight | Kristina Monakhova, Stephan R. Richter, Laura Waller, Vladlen Koltun | Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we demonstrate photorealistic video under starlight (no moon present, <0.001 lux) for the first time. To enable this, we develop a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light levels. Using this noise model, we train a video denoiser using a combination of simulated noisy video clips and real noisy still images. We capture a 5-10 fps video dataset with significant motion at approximately 0.6-0.7 millilux with no active illumination. Comparing against alternative methods, we achieve improved video quality at the lowest light levels, demonstrating photorealistic video denoising in starlight for the first time. | https://openaccess.thecvf.com/content/CVPR2022/papers/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Monakhova_Dancing_Under_the_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.04210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.html | CVPR 2022 | null |
FocusCut: Diving Into a Focus View in Interactive Segmentation | Zheng Lin, Zheng-Peng Duan, Zhao Zhang, Chun-Le Guo, Ming-Ming Cheng | Interactive image segmentation is an essential tool in pixel-level annotation and image editing. To obtain a high-precision binary segmentation mask, users tend to add interaction clicks around the object details, such as edges and holes, for efficient refinement. Current methods regard these repair clicks as the guidance to jointly determine the global prediction. However, the global view makes the model lose focus from later clicks, and is not in line with user intentions. In this paper, we dive into the view of clicks' eyes to endow them with the decisive role in object details again. To verify the necessity of focus view, we design a simple yet effective pipeline, named FocusCut, which integrates the functions of object segmentation and local refinement. After obtaining the global prediction, it crops click-centered patches from the original image with adaptive scopes to refine the local predictions progressively. Without user perception and parameters increase, our method has achieved state-of-the-art results. Extensive experiments and visualized results demonstrate that FocusCut makes hyper-fine segmentation possible for interactive image segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Medial Spectral Coordinates for 3D Shape Analysis | Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar, Herve Lombaert, Michael Gruninger, Dirk Walther, Kaleem Siddiqi | In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision to autonomous driving, medical imaging, and robotics. In these settings, spectral coordinates have shown promise for shape representation due to their ability to incorporate both local and global shape properties in a manner that is qualitatively invariant to isometric transformations. Yet, surprisingly, such coordinates have thus far typically considered only local surface positional or derivative information. In the present article, we propose to equip spectral coordinates with medial (object width) information, so as to enrich them. The key idea is to couple surface points that share a medial ball, via the weights of the adjacency matrix. We develop a spectral feature using this idea, and the algorithms to compute it. The incorporation of object width and medial coupling has direct benefits, as illustrated by our experiments on object classification, object part segmentation, and surface point correspondence. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13295 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Contextualized Spatio-Temporal Contrastive Learning With Self-Supervision | Liangzhe Yuan, Rui Qian, Yin Cui, Boqing Gong, Florian Schroff, Ming-Hsuan Yang, Hartwig Adam, Ting Liu | Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes suboptimal for learning spatio-temporally fine-grained features in videos, where scenes and instances evolve through space and time. In this paper, we present Contextualized Spatio-Temporal Contrastive Learning (ConST-CL) to effectively learn spatio-temporally fine-grained video representations via self-supervision. We first design a region-based pretext task which requires the model to transform instance representations from one view to another, guided by context features. Further, we introduce a simple network design that successfully reconciles the simultaneous learning process of both holistic and local representations. We evaluate our learned representations on a variety of downstream tasks and show that ConST-CL achieves competitive results on 6 datasets, including Kinetics, UCF, HMDB, AVAKinetics, AVA and OTB. Our code and models will be available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_Contextualized_Spatio-Temporal_Contrastive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin | Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic forgetting across real-world heterogeneous devices. In this paper, we demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. Concretely, we conduct the first rigorous empirical investigation of different neural architectures across a range of federated algorithms, real-world benchmarks, and heterogeneous data splits. Our experiments show that simply replacing convolutional networks with Transformers can greatly reduce catastrophic forgetting of previous devices, accelerate convergence, and reach a better global model, especially when dealing with heterogeneous data. We will release our code and pretrained models to encourage future exploration in robust architectures as an alternative to current research efforts on the optimization front. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qu_Rethinking_Architecture_Design_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.06047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
APES: Articulated Part Extraction From Sprite Sheets | Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi, Evangelos Kalogerakis | Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated parts, e.g. head, torso and limbs, that can be re-assembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively. Our project page https://zhan-xu.github.io/parts/ includes our code and data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_APES_Articulated_Part_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.html | CVPR 2022 | null |
Dressing in the Wild by Watching Dance Videos | Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, Jianchao Yang | While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details. This paper, therefore, attends to virtual try-on in real-world scenes and brings essential improvements in authenticity and naturalness especially for loose garment (e.g., skirts, formal dresses), challenging poses (e.g., cross arms, bent legs), and cluttered backgrounds. Specifically, we find that the pixel flow excels at handling loose garments whereas the vertex flow is preferred for hard poses, and by combining their advantages we propose a novel generative network called wFlow that can effectively push up garment transfer to in-the-wild context. Moreover, former approaches require paired images for training. Instead, we cut down the laboriousness by working on a newly constructed large-scale video dataset named Dance50k with self-supervised cross-frame training and an online cycle optimization. The proposed Dance50k can boost real-world virtual dressing by covering a wide variety of garments under dancing poses. Extensive experiments demonstrate the superiority of our wFlow in generating realistic garment transfer results for in-the-wild images without resorting to expensive paired datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Dressing_in_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15320 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
SPAct: Self-Supervised Privacy Preservation for Action Recognition | Ishan Rajendrakumar Dave, Chen Chen, Mubarak Shah | Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames of video dataset for privacy labels is not feasible. Recent developments of self-supervised learning (SSL) have unleashed the untapped potential of the unlabeled data. For the first time, we present a novel training framework which removes privacy information from input video in a self-supervised manner without requiring privacy labels. Our training framework consists of three main components: anonymization function, self-supervised privacy removal branch, and action recognition branch. We train our framework using a minimax optimization strategy to minimize the action recognition cost function and maximize the privacy cost function through a contrastive self-supervised loss. Employing existing protocols of known-action and privacy attributes, our framework achieves a competitive action-privacy trade-off to the existing state-of-the-art supervised methods. In addition, we introduce a new protocol to evaluate the generalization of learned the anonymization function to novel-action and privacy attributes and show that our self-supervised framework outperforms existing supervised methods. Code available at: https://github.com/DAVEISHAN/SPAct | https://openaccess.thecvf.com/content/CVPR2022/papers/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dave_SPAct_Self-Supervised_Privacy_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15205 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Uni6D: A Unified CNN Framework Without Projection Breakdown for 6D Pose Estimation | Xiaoke Jiang, Donghai Li, Hao Chen, Ye Zheng, Rui Zhao, Liwei Wu | As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use different backbones to extract features for RGB and depth images. They use a 2D CNN for RGB images and a per-pixel point cloud network for depth data, as well as a fusion network for feature fusion. We find that the essential reason for using two independent backbones is the "projection breakdown" problem. In the depth image plane, the projected 3D structure of the physical world is preserved by the 1D depth value and its built-in 2D pixel coordinate (UV). Any spatial transformation that modifies UV, such as resize, flip, crop, or pooling operations in the CNN pipeline, breaks the binding between the pixel value and UV coordinate. As a consequence, the 3D structure is no longer preserved by a modified depth image or feature. To address this issue, we propose a simple yet effective method denoted as Uni6D that explicitly takes the extra UV data along with RGB-D images as input. Our method has a Unified CNN framework for 6D pose estimation with a single CNN backbone. In particular, the architecture of our method is based on Mask R-CNN with two extra heads, one named RT head for directly predicting 6D pose and the other named abc head for guiding the network to map the visible points to their coordinates in the 3D model as an auxiliary module. This end-to-end approach balances simplicity and accuracy, achieving comparable accuracy with state of the arts and 7.2x faster inference speed on the YCB-Video dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Uni6D_A_Unified_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.html | CVPR 2022 | null |
De-Rendering 3D Objects in the Wild | Felix Wimbauer, Shangzhe Wu, Christian Rupprecht | With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks. Large-scale deployment of XR devices and applications means that we cannot solely rely on supervised learning, as collecting and annotating data for the unlimited variety of objects in the real world is infeasible. We present a weakly supervised method that is able to decompose a single image of an object into shape (depth and normals), material (albedo, reflectivity and shininess) and global lighting parameters. For training, the method only relies on a rough initial shape estimate of the training objects to bootstrap the learning process. This shape supervision can come for example from a pretrained depth network or - more generically - from a traditional structure-from-motion pipeline. In our experiments, we show that the method can successfully de-render 2D images into a decomposed 3D representation and generalizes to unseen object categories. Since in-the-wild evaluation is difficult due to the lack of ground truth data, we also introduce a photo-realistic synthetic test set that allows for quantitative evaluation. Please find our project page at: https://github.com/Brummi/derender3d | https://openaccess.thecvf.com/content/CVPR2022/papers/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wimbauer_De-Rendering_3D_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02279 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
SPAMs: Structured Implicit Parametric Models | Pablo Palafox, Nikolaos Sarafianos, Tony Tung, Angela Dai | Parametric 3D models have formed a fundamental role in modeling deformable objects, such as human bodies, faces, and hands; however, the construction of such parametric models requires significant manual intervention and domain expertise. Recently, neural implicit 3D representations have shown great expressibility in capturing 3D shape geometry. We observe that deformable object motion is often semantically structured, and thus propose to learn Structured-implicit PArametric Models (SPAMs) as a deformable object representation that structurally decomposes non-rigid object motion into part-based disentangled representations of shape and pose, with each being represented by deep implicit functions. This enables a structured characterization of object movement, with part decomposition characterizing a lower-dimensional space in which we can establish coarse motion correspondence. In particular, we can leverage the part decompositions at test time to fit to new depth sequences of unobserved shapes, by establishing part correspondences between the input observation and our learned part spaces; this guides a robust joint optimization between the shape and pose of all parts, even under dramatic motion sequences. Experiments demonstrate that our part-aware shape and pose understanding lead to state-of-the-art performance in reconstruction and tracking of depth sequences of complex deforming object motion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Palafox_SPAMs_Structured_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.08141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Global Sensing and Measurements Reuse for Image Compressed Sensing | Zi-En Fan, Feng Lian, Jia-Ni Quan | Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use it only once for image reconstruction. They ignore there are low, mid, and high-level features in the network and all of them are essential for high-quality reconstruction. Moreover, using measurements only once may not be enough for extracting richer information from measurements. To address these issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all level features for achieving an efficient sensing and Measurements Reuse Block (MRB) to reuse measurements multiple times on multi-scale. Finally, we conduct a series of experiments on three benchmark datasets to show that our model can significantly outperform state-of-the-art methods. Code is available at https://github.com/fze0012/MR-CCSNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.pdf | null | https://arxiv.org/abs/2206.11629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
SeeThroughNet: Resurrection of Auxiliary Loss by Preserving Class Probability Information | Dasol Han, Jaewook Yoo, Dokwan Oh | Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary loss between the feature maps of intermediate layers and the ground truth in the field of semantic segmentation, the size of each feature map must match the ground truth. In all studies using the auxiliary losses with the segmentation models, from what we have investigated, they either use a down-sampling function to reduce the size of the ground truth or use an up-sampling function to increase the size of the feature map in order to match the resolution between the feature map and the ground truth. However, in the process of selecting representative values through down-sampling and up-sampling, information loss is inevitable. In this paper, we introduce Class Probability Preserving (CPP) pooling to alleviate information loss in down-sampling the ground truth in semantic segmentation tasks. We demonstrated the superiority of the proposed method on Cityscapes, Pascal VOC, Pascal Context, and NYU-Depth-v2 datasets by using CPP pooling with auxiliary losses based on seven popular segmentation models. In addition, we propose See-Through Network (SeeThroughNet) that adopts an improved multi-scale attention-coupled decoder structure to maximize the effect of CPP pooling. SeeThroughNet shows cutting-edge results in the field of semantic understanding of urban street scenes, which ranked #1 on the Cityscapes benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_SeeThroughNet_Resurrection_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.html | CVPR 2022 | null |
Representing 3D Shapes With Probabilistic Directed Distance Fields | Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan D. Jepson | Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Yet, explicit shape representations (e.g., voxels, point clouds, meshes), while relatively easily rendered, often suffer from limited geometric fidelity or topological constraints. On the other hand, implicit representations (e.g., occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability. In this work, we endeavour to address both shortcomings with a novel shape representation that allows fast differentiable rendering within an implicit architecture. Building on implicit distance representations, we define Directed Distance Fields (DDFs), which map an oriented point (position and direction) to surface visibility and depth. Such a field can render a depth map with a single forward pass per pixel, enable differential surface geometry extraction (e.g., surface normals and curvatures) via network derivatives, can be easily composed, and permit extraction of classical unsigned distance fields. Using probabilistic DDFs (PDDFs), we show how to model inherent discontinuities in the underlying field. Finally, we apply our method to fitting single shapes, unpaired 3D-aware generative image modelling, and single-image 3D reconstruction tasks, showcasing strong performance with simple architectural components via the versatility of our representation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Aumentado-Armstrong_Representing_3D_Shapes_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Learning ABCs: Approximate Bijective Correspondence for Isolating Factors of Variation With Weak Supervision | Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia | Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set. Our key insight is that by seeking correspondence between elements of different sets, we learn strong representations that exclude the inactive factors of variation and isolate the active factors that vary within all sets. As a consequence of focusing on the active factors, our method can leverage a mix of set-supervised and wholly unsupervised data, which can even belong to a different domain. We tackle the challenging problem of synthetic-to-real object pose transfer, without pose annotations on anything, by isolating pose information which generalizes to the category level and across the synthetic/real domain gap. The method can also boost performance in supervised settings, by strengthening intermediate representations, as well as operate in practically attainable scenarios with set-supervised natural images, where quantity is limited and nuisance factors of variation are more plentiful. | https://openaccess.thecvf.com/content/CVPR2022/papers/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Murphy_Learning_ABCs_Approximate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.03240 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.html | CVPR 2022 | null |
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding | Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F. Yago Vicente, Thomas Dideriksen, Himanshu Arora, Matthieu Guillaumin, Jitendra Malik | We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval. | https://openaccess.thecvf.com/content/CVPR2022/papers/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Collins_ABO_Dataset_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.06199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
DETReg: Unsupervised Pretraining With Region Priors for Object Detection | Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson | Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture. Instead, we introduce DETReg, a new self-supervised method that pretrains the entire object detection network, including the object localization and embedding components. During pretraining, DETReg predicts object localizations to match the localizations from an unsupervised region proposal generator and simultaneously aligns the corresponding feature embeddings with embeddings from a self-supervised image encoder. We implement DETReg using the DETR family of detectors and show that it improves over competitive baselines when finetuned on COCO, PASCAL VOC, and Airbus Ship benchmarks. In low-data regimes, including semi-supervised and few-shot learning settings, DETReg establishes many state-of-the-art results, e.g., on COCO we see a +6.0 AP improvement for 10-shot detection and +3.5 AP improvement when training with only 1% of the labels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bar_DETReg_Unsupervised_Pretraining_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04550 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Restore 3D Face From In-the-Wild Degraded Images | Zhenyu Zhang, Yanhao Ge, Ying Tai, Xiaoming Huang, Chengjie Wang, Hao Tang, Dongjin Huang, Zhifeng Xie | In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsupervised high-quality face reconstruction from low-resolution images. Rather than directly refining 2D image appearance, L2R learns to recover fine-grained 3D details on the proxy against degradation via extracting generative facial priors. Concretely, L2R proposes a novel albedo restoration network to model high-quality 3D facial texture, in which the diverse guidance from the pre-trained Generative Adversarial Networks (GANs) is leveraged to complement the lack of input facial clues. With the finer details of the restored 3D texture, L2R then learns displacement maps from scratch to enhance the significant facial structure and geometry. Both of the procedures are mutually optimized with a novel 3D-aware adversarial loss, which further improves the modelling performance and suppresses the potential uncertainty. Extensive experiments on benchmarks show that L2R outperforms state-of-the-art methods under the condition of low-quality inputs, and obtains superior performances than 2D pre-processed modelling approaches with limited 3D proxy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Learning_To_Restore_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack | Ye Liu, Yaya Cheng, Lianli Gao, Xianglong Liu, Qilong Zhang, Jingkuan Song | Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for defense models' lower bound of robustness given a budget number of iterations and a test dataset. A practical evaluation method should be convenient (i.e., parameter-free), efficient (i.e., fewer iterations) and reliable (i.e., approaching the lower bound of robustness). Towards this target, we propose a parameter-free Adaptive Auto Attack (A3) evaluation method which addresses the efficiency and reliability in a test-time-training fashion. Specifically, by observing that adversarial examples to a specific defense model follow some regularities in their starting points, we design an Adaptive Direction Initialization strategy to speed up the evaluation. Furthermore, to approach the lower bound of robustness under the budget number of iterations, we propose an online statistics-based discarding strategy that automatically identifies and abandons hard-to-attack images. Extensive experiments on nearly 50 widely-used defense models demonstrate the effectiveness of our A3.By consuming much fewer iterations than existing methods, i.e., 1/10 on average (10x speed up), we achieve lower robust accuracy in all cases. Notably, we won first place out of 1681 teams in CVPR 2021 White-box Adversarial Attacks on Defense Models competitions with this method. Code is available at: https://github.com/liuye6666/adaptive_auto_attack | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Practical_Evaluation_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Convolutions for Spatial Interaction Modeling | Zhaoen Su, Chao Wang, David Bradley, Carlos Vallespi-Gonzalez, Carl Wellington, Nemanja Djuric | In many different fields interactions between objects play a critical role in determining their behavior. Graph neural networks (GNNs) have emerged as a powerful tool for modeling interactions, although often at the cost of adding considerable complexity and latency. In this paper, we consider the problem of spatial interaction modeling in the context of predicting the motion of actors around autonomous vehicles, and investigate alternatives to GNNs. We revisit 2D convolutions and show that they can demonstrate comparable performance to graph networks in modeling spatial interactions with lower latency, thus providing an effective and efficient alternative in time-critical systems. Moreover, we propose a novel interaction loss to further improve the interaction modeling of the considered methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Su_Convolutions_for_Spatial_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2104.07182 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection | Rui Dai, Srijan Das, Kumara Kahatapitiya, Michael S. Ryoo, François Brémond | Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos. The temporal relation is complex in those datasets, including challenges like composite action, and co-occurring action. For detecting actions in those complex videos, efficiently capturing both short-term and long-term temporal information in the video is critical. To this end, we propose a novel ConvTransformer network for action detection. This network comprises three main components: (1) Temporal Encoder module extensively explores global and local temporal relations at multiple temporal resolutions. (2) Temporal Scale Mixer module effectively fuses the multi-scale features to have a unified feature representation. (3) Classification module is used to learn the instance center-relative position and predict the frame-level classification scores. The extensive experiments on multiple datasets, including Charades, TSU and MultiTHUMOS, confirm the effectiveness of our proposed method. Our network outperforms the state-of-the-art methods on all the three datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dai_MS-TCT_Multi-Scale_Temporal_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Salvage of Supervision in Weakly Supervised Object Detection | Lin Sui, Chen-Lin Zhang, Jianxin Wu | Weakly supervised object detection (WSOD) has recently attracted much attention. However, the lack of bounding-box supervision makes its accuracy much lower than fully supervised object detection (FSOD), and currently modern FSOD techniques cannot be applied to WSOD. To bridge the performance and technical gaps between WSOD and FSOD, this paper proposes a new framework, Salvage of Supervision (SoS), with the key idea being to harness every potentially useful supervisory signal in WSOD: the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection. This paper shows that each type of supervisory signal brings in notable improvements, outperforms existing WSOD methods (which mainly use only the weak labels) by large margins. The proposed SoS-WSOD method also have the ability to freely use modern FSOD techniques. SoS-WSOD achieves 64.4 mAP50 on VOC2007, 61.9 mAP50 on VOC2012 and 16.6 mAP50:95 on MS-COCO, and also has fast inference speed. Ablations and visualization further verify the effectiveness of SoS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sui_Salvage_of_Supervision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-View Transformers for Real-Time Map-View Semantic Segmentation | Brady Zhou, Philipp Krähenbühl | We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positional embeddings that depend on its intrinsic and extrinsic calibration. These embeddings allow a transformer to learn the mapping across different views without ever explicitly modeling it geometrically. The architecture consists of a convolutional image encoder for each view and cross-view transformer layers to infer a map-view semantic segmentation. Our model is simple, easily parallelizable, and runs in real-time. The presented architecture performs at state-of-the-art on the nuScenes dataset, with 4x faster inference speeds. Code is available at https://github.com/bradyz/cross_view_transformers. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning | Hongzu Su, Jingjing Li, Zhi Chen, Lei Zhu, Ke Lu | Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Recognizing unseen classes as seen ones or vice versa often leads to poor performance in GZSL. Therefore, distinguishing seen and unseen domains is naturally an effective yet challenging solution for GZSL. In this paper, we present a novel method which leverages both visual and semantic modalities to distinguish seen and unseen categories. Specifically, our method deploys two variational autoencoders to generate latent representations for visual and semantic modalities in a shared latent space, in which we align latent representations of both modalities by Wasserstein distance and reconstruct two modalities with the representations of each other. In order to learn a clearer boundary between seen and unseen classes, we propose a two-stage training strategy which takes advantage of seen and unseen semantic descriptions and searches a threshold to separate seen and unseen visual samples. At last, a seen expert and an unseen expert are used for final classification. Extensive experiments on five widely used benchmarks verify that the proposed method can significantly improve the results of GZSL. For instance, our method correctly recognizes more than 99% samples when separating domains and improves the final classification accuracy from 72.6% to 82.9% on AWA1. | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Online Continual Learning on a Contaminated Data Stream With Blurry Task Boundaries | Jihwan Bang, Hyunseo Koh, Seulki Park, Hwanjun Song, Jung-Woo Ha, Jonghyun Choi | Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. Large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored. We consider a more practical CL setup of an online learning from blurry data stream with corrupted noise, where existing CL methods struggle. To address the task, we first argue the importance of both diversity and purity of examples in the episodic memory of continual learning models. To balance diversity and purity in the episodic memory, we propose a novel strategy to manage and use the memory by a unified approach of label noise aware diverse sampling and robust learning with semi-supervised learning. Our empirical validations on four real-world or synthetic benchmark datasets (CIFAR10 and 100, mini-WebVision, and Food-101N) show that our method significantly outperforms prior arts in this realistic and challenging continual learning scenario. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bang_Online_Continual_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15355 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.html | CVPR 2022 | null |
Controllable Dynamic Multi-Task Architectures | Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K. Roy-Chowdhury, Manmohan Chandraker | Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over the relative importance of tasks and total compute cost during inference time. In this work, we propose such a controllable multi-task network that dynamically adjusts its architecture and weights to match the desired task preference as well as the resource constraints. In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better. We propose a disentangled training of two hypernetworks, by exploiting task affinity and a novel branching regularized loss, to take input preferences and accordingly predict tree-structured models with adapted weights. Experiments on three multi-task benchmarks, namely PASCAL-Context, NYU-v2, and CIFAR-100, show the efficacy of our approach. Project page is available at https://www.nec-labs.com/ mas/DYMU. | https://openaccess.thecvf.com/content/CVPR2022/papers/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Raychaudhuri_Controllable_Dynamic_Multi-Task_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14949 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Imagine: Diversify Memory for Incremental Learning Using Unlabeled Data | Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng | Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications. Although maintaining a handful of samples (called "exemplars") of each task could alleviate forgetting to some extent, existing methods are still limited by the small number of exemplars since these exemplars are too few to carry enough task-specific knowledge, and therefore the forgetting remains. To overcome this problem, we propose to "imagine" diverse counterparts of given exemplars referring to the abundant semantic-irrelevant information from unlabeled data. Specifically, we develop a learnable feature generator to diversify exemplars by adaptively generating diverse counterparts of exemplars based on semantic information from exemplars and semantically-irrelevant information from unlabeled data. We introduce semantic contrastive learning to enforce the generated samples to be semantic consistent with exemplars and perform semanticdecoupling contrastive learning to encourage diversity of generated samples. The diverse generated samples could effectively prevent DNN from forgetting when learning new tasks. Our method does not bring any extra inference cost and outperforms state-of-the-art methods on two benchmarks CIFAR-100 and ImageNet-Subset by a clear margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Learning_To_Imagine_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.html | CVPR 2022 | null |
SmartAdapt: Multi-Branch Object Detection Framework for Videos on Mobiles | Ran Xu, Fangzhou Mu, Jayoung Lee, Preeti Mukherjee, Somali Chaterji, Saurabh Bagchi, Yin Li | Several recent works seek to create lightweight deep networks for video object detection on mobiles. We observe that many existing detectors, previously deemed computationally costly for mobiles, intrinsically support adaptive inference, and offer a multi-branch object detection framework (MBODF). Here, an MBODF is referred to as a solution that has many execution branches and one can dynamically choose from among them at inference time to satisfy varying latency requirements (e.g. by varying resolution of an input frame). In this paper, we ask, and answer, the wide-ranging question across all MBODFs: How to expose the right set of execution branches and then how to schedule the optimal one at inference time? In addition, we uncover the importance of making a content-aware decision on which branch to run, as the optimal one is conditioned on the video content. Finally, we explore a content-aware scheduler, an Oracle one, and then a practical one, leveraging various lightweight feature extractors. Our evaluation shows that layered on Faster R-CNN-based MBODF, compared to 7 baselines, our SMARTADAPT achieves a higher Pareto optimal curve in the accuracy-vs-latency space for the ILSVRC VID dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_SmartAdapt_Multi-Branch_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.html | CVPR 2022 | null |
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks | Yi-Lin Sung, Jaemin Cho, Mohit Bansal | Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this paper, we introduce adapter-based parameter-efficient transfer learning techniques to V&L models such as VL-BART and VL-T5. We evaluate our methods in a unified multi-task setup on both image-text and video-text benchmarks. For the image-text tasks, we use four diverse V&L datasets: VQAv2, GQA, NLVR2, and MSCOCO image captioning. For video-text tasks, we use TVQA, How2QA, TVC, and YC2C. With careful training and thorough experiments, we benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter) against the standard full fine-tuning and the recently proposed prompt-tuning approach. We also enhance the efficiency and performance of adapters by sharing their weights to attain knowledge across tasks. Our results demonstrate that training the adapter with the weight-sharing technique (4.18% of total parameters for image-text tasks and 3.39% for video-text tasks) can match the performance of fine-tuning the entire model. Lastly, we present a comprehensive analysis including the combination of adapter and task-specific prompts and the impact of V&L pre-training on adapters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sung_VL-Adapter_Parameter-Efficient_Transfer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Hybrid Models for Out-of-Distribution Detection | Senqi Cao, Zhongfei Zhang | We propose a principled and practical method for out-of-distribution (OoD) detection with deep hybrid models (DHMs), which model the joint density p(x,y) of features and labels with a single forward pass. By factorizing the joint density p(x,y) into three sources of uncertainty, we show that our approach has the ability to identify samples semantically different from the training data. To ensure computational scalability, we add a weight normalization step during training, which enables us to plug in state-of-the-art (SoTA) deep neural network (DNN) architectures for approximately modeling and inferring expressive probability distributions. Our method provides an efficient, general, and flexible framework for predictive uncertainty estimation with promising results and theoretical support. To our knowledge, this is the first work to reach 100% in OoD detection tasks on both vision and language datasets, especially on notably difficult dataset pairs such as CIFAR-10 vs. SVHN and CIFAR-100 vs. CIFAR-10. This work is a step towards enabling DNNs in real-world deployment for safety-critical applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_Deep_Hybrid_Models_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Accelerating Video Object Segmentation With Compressed Video | Kai Xu, Angela Yao | We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose a motion vector-based warping method for propagating segmentation masks from keyframes to other frames in a bi-directional and multi-hop manner. Additionally, we introduce a residual-based correction module that can fix wrongly propagated segmentation masks from noisy or erroneous motion vectors. Our approach is flexible and can be added on top of several existing video object segmentation algorithms. We achieved highly competitive results on DAVIS17 and YouTube-VOS on various base models with substantial speed-ups of up to 3.5X with minor drops in accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Accelerating_Video_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.12192 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation | Fan Wang, Zhongyi Han, Yongshun Gong, Yilong Yin | Source-free domain adaptation (SFDA) newly emerges to transfer the relevant knowledge of a well-trained source model to an unlabeled target domain, which is critical in various privacy-preserving scenarios. Most existing methods focus on learning the domain-invariant representations depending solely on the target data, leading to the obtained representations are target-specific. In this way, they cannot fully address the distribution shift problem across domains. In contrast, we provide a fascinating insight: rather than attempting to learn domain-invariant representations, it is better to explore the domain-invariant parameters of the source model. The motivation behind this insight is clear: the domain-invariant representations are dominated by only partial parameters of an available deep source model. We devise the Domain-Invariant Parameter Exploring (DIPE) approach to capture such domain-invariant parameters in the source model to generate domain-invariant representations. A distinguishing method is developed correspondingly for two types of parameters, i.e., domain-invariant and domain-specific parameters, as well as an effective update strategy based on the clustering correction technique and a target hypothesis is proposed. Extensive experiments verify that DIPE successfully exceeds the current state-of-the-art models on many domain adaptation datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
FastDOG: Fast Discrete Optimization on GPU | Ahmed Abbas, Paul Swoboda | We present a massively parallel Lagrange decomposition method for solving 0--1 integer linear programs occurring in structured prediction. We propose a new iterative update scheme for solving the Lagrangean dual and a perturbation technique for decoding primal solutions. For representing subproblems we follow Lange et al. (2021) and use binary decision diagrams (BDDs). Our primal and dual algorithms require little synchronization between subproblems and optimization over BDDs needs only elementary operations without complicated control flow. This allows us to exploit the parallelism offered by GPUs for all components of our method. We present experimental results on combinatorial problems from MAP inference for Markov Random Fields, quadratic assignment and cell tracking for developmental biology. Our highly parallel GPU implementation improves upon the running times of the algorithms from Lange et al. (2021) by up to an order of magnitude. In particular, we come close to or outperform some state-of-the-art specialized heuristics while being problem agnostic. Our implementation is available at https://github.com/LPMP/BDD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Abbas_FastDOG_Fast_Discrete_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.html | CVPR 2022 | null |
Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction | Sara Elkerdawy, Mostafa Elhoushi, Hong Zhang, Nilanjan Ray | Dynamic model pruning is a recent direction that allows for the inference of a different sub-network for each input sample during deployment. However, current dynamic methods rely on learning a continuous channel gating through regularization by inducing sparsity loss. This formulation introduces complexity in balancing different losses (e.g task loss, regularization loss). In addition, regularization based methods lack transparent tradeoff hyperparameter selection to realize computational budget. Our contribution is two-fold: 1) decoupled task and pruning training. 2) Simple hyperparameter selection that enables FLOPs reduction estimation before training. Inspired by the Hebbian theory in Neuroscience: "neurons that fire together wire together", we propose to predict a mask to process k filters in a layer based on the activation of its previous layer. We pose the problem as a self-supervised binary classification problem. Each mask predictor module is trained to predict if the log-likelihood for each filter in the current layer belongs to the top-k activated filters. The value k is dynamically estimated for each input based on a novel criterion using the mass of heatmaps. We show experiments on several neural architectures, such as VGG, ResNet and MobileNet on CIFAR and ImageNet datasets. On CIFAR, we reach similar accuracy to SOTA methods with 15% and 24% higher FLOPs reduction. Similarly in ImageNet, we achieve lower drop in accuracy with up to 13% improvement in FLOPs reduction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Elkerdawy_Fire_Together_Wire_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.08232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection | Yifan Wang, Wenbo Zhang, Lijun Wang, Ting Liu, Huchuan Lu | Deep learning-based image salient object detection (SOD) heavily relies on large-scale training data with pixel-wise labeling. High-quality labels involve intensive labor and are expensive to acquire. In this paper, we propose a novel multi-source uncertainty mining method to facilitate unsupervised deep learning from multiple noisy labels generated by traditional handcrafted SOD methods. We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label. Meanwhile, we model the noisy labels using Gibbs distribution and propose a weighted uncertainty loss to jointly train the UMNet with the SOD network. As a consequence, our UMNet can adaptively select reliable labels for SOD network learning. Extensive experiments on benchmark datasets demonstrate that our method not only outperforms existing unsupervised methods, but also is on par with fully-supervised state-of-the-art models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Equivariant Learning for Oriented Keypoint Detection | Jongmin Lee, Byungjin Kim, Minsu Cho | Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but often fail to detect reliable keypoints against geometric variations. To learn to detect robust oriented keypoints, we introduce a self-supervised learning framework using rotation-equivariant CNNs. We propose a dense orientation alignment loss by an image pair generated by synthetic transformations for training a histogram-based orientation map. Our method outperforms the previous methods on an image matching benchmark and a camera pose estimation benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Self-Supervised_Equivariant_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation | Linfeng Zhang, Xin Chen, Xiaobing Tu, Pengfei Wan, Ning Xu, Kaisheng Ma | Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-the-art GANs usually suffer from low efficiency and bulky memory usage. To tackle this challenge, firstly, this paper investigates GANs performance from a frequency perspective. The results show that GANs, especially small GANs lack the ability to generate high-quality high frequency information. To address this problem, we propose a novel knowledge distillation method referred to as wavelet knowledge distillation. Instead of directly distilling the generated images of teachers, wavelet knowledge distillation first decomposes the images into different frequency bands with discrete wavelet transformation and then only distills the high frequency bands. As a result, the student GAN can pay more attention to its learning on high frequency bands. Experiments demonstrate that our method leads to 7.08X compression and 6.80X acceleration on CycleGAN with almost no performance drop. Additionally, we have studied the relation between discriminators and generators which shows that the compression of discriminators can promote the performance of compressed generators. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.06321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Focal and Global Knowledge Distillation for Detectors | Zhendong Yang, Zhe Li, Xiaohu Jiang, Yuan Gong, Zehuan Yuan, Danpei Zhao, Chun Yuan | Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it. In this paper, we point out that in object detection, the features of the teacher and student vary greatly in different areas, especially in the foreground and background. If we distill them equally, the uneven differences between feature maps will negatively affect the distillation. Thus, we propose Focal and Global Distillation (FGD). Focal distillation separates the foreground and background, forcing the student to focus on the teacher's critical pixels and channels. Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation. As our method only needs to calculate the loss on the feature map, FGD can be applied to various detectors. We experiment on various detectors with different backbones and the results show that the student detector achieves excellent mAP improvement. For example, ResNet-50 based RetinaNet, Faster RCNN, RepPoints and Mask RCNN with our distillation method achieve 40.7%, 42.0%, 42.0% and 42.1% mAP on COCO2017, which are 3.3, 3.6, 3.4 and 2.9 higher than the baseline, respectively. Our codes are available at https://github.com/yzd-v/FGD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.11837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Prompt for Continual Learning | Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister | The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and address forgetting, while this work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time. Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions. In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space. The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity. We conduct comprehensive experiments under popular image classification benchmarks with different challenging continual learning settings, where L2P consistently outperforms prior state-of-the-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer and is directly applicable to challenging task-agnostic continual learning. Source code is available at https://github.com/google-research/l2p. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Learning_To_Prompt_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.08654 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Human Mesh Recovery From Multiple Shots | Georgios Pavlakos, Jitendra Malik, Angjoo Kanazawa | Videos from edited media like movies are a useful, yet under-explored source of information, with rich variety of appearance and interactions between humans depicted over a large temporal context. However, the richness of data comes at the expense of fundamental challenges such as abrupt shot changes and close up shots of actors with heavy truncation, which limits the applicability of existing 3D human understanding methods. In this paper, we address these limitations with the insight that while shot changes of the same scene incur a discontinuity between frames, the 3D structure of the scene still changes smoothly. This allows us to handle frames before and after the shot change as multi-view signal that provide strong cues to recover the 3D state of the actors. We propose a multi-shot optimization framework that realizes this insight, leading to improved 3D reconstruction and mining of sequences with pseudo-ground truth 3D human mesh. We treat this data as valuable supervision for models that enable human mesh recovery from movies; both from single image and from video, where we propose a transformer-based temporal encoder that can naturally handle missing observations due to shot changes in the input frames. We demonstrate the importance of our insight and proposed models through extensive experiments. The tools we develop open the door to processing and analyzing in 3D content from a large library of edited media, which could be helpful for many downstream applications. Code, models and data are available at: https://geopavlakos.github.io/multishot/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pavlakos_Human_Mesh_Recovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2012.09843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Adversarial Transferability via Neuron Attribution-Based Attacks | Jianping Zhang, Weibin Wu, Jen-tse Huang, Yizhan Huang, Wenxuan Wang, Yuxin Su, Michael R. Lyu | Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To efficiently tackle the black-box setting where the target model's particulars are unknown, feature-level transfer-based attacks propose to contaminate the intermediate feature outputs of local models, and then directly employ the crafted adversarial samples to attack the target model. Due to the transferability of features, feature-level attacks have shown promise in synthesizing more transferable adversarial samples. However, existing feature-level attacks generally employ inaccurate neuron importance estimations, which deteriorates their transferability. To overcome such pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA), which conducts feature-level attacks with more accurate neuron importance estimations. Specifically, we first completely attribute a model's output to each neuron in a middle layer. We then derive an approximation scheme of neuron attribution to tremendously reduce the computation overhead. Finally, we weight neurons based on their attribution results and launch feature-level attacks. Extensive experiments confirm the superiority of our approach to the state-of-the-art benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Improving_Adversarial_Transferability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00008 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.html | CVPR 2022 | null |
Better Trigger Inversion Optimization in Backdoor Scanning | Guanhong Tao, Guangyu Shen, Yingqi Liu, Shengwei An, Qiuling Xu, Shiqing Ma, Pan Li, Xiangyu Zhang | Backdoor attacks aim to cause misclassification of a subject model by stamping a trigger to inputs. Backdoors could be injected through malicious training and naturally exist. Deriving backdoor trigger for a subject model is critical to both attack and defense. A popular trigger inversion method is by optimization. Existing methods are based on finding a smallest trigger that can uniformly flip a set of input samples by minimizing a mask. The mask defines the set of pixels that ought to be perturbed. We develop a new optimization method that directly minimizes individual pixel changes, without using a mask. Our experiments show that compared to existing methods, the new one can generate triggers that require a smaller number of input pixels to be perturbed, have a higher attack success rate, and are more robust. They are hence more desirable when used in real-world attacks and more effective when used in defense. Our method is also more cost-effective. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tao_Better_Trigger_Inversion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.html | CVPR 2022 | null |
GANSeg: Learning To Segment by Unsupervised Hierarchical Image Generation | Xingzhe He, Bastian Wandt, Helge Rhodin | Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of videos, and image augmentation, which limits their applicability. To address this, we propose a GAN-based approach that generates images conditioned on latent masks, thereby alleviating full or weak annotations required in previous approaches. We show that such mask-conditioned image generation can be learned faithfully when conditioning the masks in a hierarchical manner on latent keypoints that define the position of parts explicitly. Without requiring supervision of masks or points, this strategy increases robustness to viewpoint and object positions changes. It also lets us generate image-mask pairs for training a segmentation network, which outperforms the state-of-the-art unsupervised segmentation methods on established benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_GANSeg_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Dense Learning Based Semi-Supervised Object Detection | Binghui Chen, Pengyu Li, Xiang Chen, Biao Wang, Lei Zhang, Xian-Sheng Hua | The ultimate goal of semi-supervised object detection (SSOD) is to facilitate the utilization and deployment of detectors in actual applications with the help of a large amount of unlabeled data. Although a few works have proposed various self-training-based methods or consistency-regularization-based methods, they all target anchor-based detectors, while ignoring the dependency on anchor-free detectors of the actual industrial deployment. To this end, in this paper, we intend to bridge the gap on anchor-free SSOD algorithm by proposing a DenSe Learning (DSL) based algorithm for SSOD. It is mainly achieved by introducing several novel techniques, including (1) Adaptive Ignoring strategy with MetaNet for assigning multi-level and accurate dense pixel-wise pseudo-labels, (2) Aggregated Teacher for producing stable and precise pseudo-labels, and (3) uncertainty consistency regularization among scales and shuffled patches for improving the generalization of the detector. In order to verify the effectiveness of our proposed method, extensive experiments have been conducted over the popular datasets MS-COCO [??] and PASCAL-VOC [??], achieving state-of-the-art performances. Codes will be available at \textcolor[rgb] 1,0,0 xxxxxxxxx . | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.07300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction | Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan | This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is reckoned with respect to the physical interactions between the object and the user. Given a malfunctional object, humans can perform mental simulations to reason about its functionality and figure out how to fix it. Inspired by this, we propose FixIt, a dataset that contains around 5k poorly-designed 3D physical objects paired with choices to fix them. To mimic humans' mental simulation process, we present FixNet, a novel framework that seamlessly incorporates perception and physical dynamics. Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix. Experimental results show that our framework outperforms baseline models by a large margin, and can generalize well to objects with similar interaction types. We will release our code and dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Fixing_Malfunctional_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Convolution of Convolution: Let Kernels Spatially Collaborate | Rongzhen Zhao, Jian Li, Zhenzhi Wu | In the biological visual pathway, especially the retina, neurons are tiled along spatial dimensions with the electrical coupling as their local association, while in a convolution layer, kernels are placed along the channel dimension singly. We propose Convolution of Convolution, associating kernels in a layer and letting them collaborate spatially. With this method, a layer can provide feature maps with extra transformations and learn its kernels together instead of isolatedly. It is only used during training, bringing in negligible extra costs; and can be re-parameterized to common convolution before testing, boosting performance gratuitously in tasks like classification, detection and segmentation. Our method works even better when large receptive fields are demanded. The code is available on site: https://github.com/Genera1Z/ConvolutionOfConvolution. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.html | CVPR 2022 | null |
Make It Move: Controllable Image-to-Video Generation With Text Descriptions | Yaosi Hu, Chong Luo, Zhenzhong Chen | Generating controllable videos conforming to user intentions is an appealing yet challenging topic in computer vision. To enable maneuverable control in line with user intentions, a novel video generation task, named Text-Image-to-Video generation (TI2V), is proposed. With both controllable appearance and motion, TI2V aims at generating videos from a static image and a text description. The key challenges of TI2V task lie both in aligning appearance and motion from different modalities, and in handling uncertainty in text descriptions. To address these challenges, we propose a Motion Anchor-based video GEnerator (MAGE) with an innovative motion anchor (MA) structure to store appearance-motion aligned representation. To model the uncertainty and increase the diversity, it further allows the injection of explicit condition and implicit randomness. Through three-dimensional axial transformers, MA is interacted with given image to generate next frames recursively with satisfying controllability and diversity. Accompanying the new task, we build two new video-text paired datasets based on MNIST and CATER for evaluation. Experiments conducted on these datasets verify the effectiveness of MAGE and show appealing potentials of TI2V task. Code and datasets are released at https://github.com/Youncy-Hu/MAGE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Make_It_Move_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02815 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.html | CVPR 2022 | null |
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection | Tong Wang, Yousong Zhu, Yingying Chen, Chaoyang Zhao, Bin Yu, Jinqiao Wang, Ming Tang | Long-tail object detection suffers from poor performance on tail categories. We reveal that the real culprit lies in the extremely imbalanced distribution of the classifier's weight norm. For conventional softmax cross-entropy loss, such imbalanced weight norm distribution yields ill conditioned decision boundary for categories which have small weight norms. To get rid of this situation, we choose to maximize the cosine similarity between the learned feature and the weight vector of target category rather than the inner-product of them. The decision boundary between any two categories is the angular bisector of their weight vectors. Whereas, the absolutely equal decision boundary is suboptimal because it reduces the model's sensitivity to various categories. Intuitively, categories with rich data diversity should occupy a larger area in the classification space while categories with limited data diversity should occupy a slightly small space. Hence, we devise a Category-Aware Angular Margin Loss (C2AM Loss) to introduce an adaptive angular margin between any two categories. Specifically, the margin between two categories is proportional to the ratio of their classifiers' weight norms. As a result, the decision boundary is slightly pushed towards the category which has a smaller weight norm. We conduct comprehensive experiments on LVIS dataset. C2AM Loss brings 4.9 5.2 AP improvements on different detectors and backbones compared with baseline. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Points: Point Cloud Representation With Neural Fields for Arbitrary Upsampling | Wanquan Feng, Jin Li, Hongrui Cai, Xiaonan Luo, Juyong Zhang | In this paper, we propose Neural Points, a novel point cloud representation and apply it to the arbitrary-factored upsampling task. Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuous geometric shape via neural fields. Therefore, Neural Points contain more shape information and thus have a stronger representation ability. Neural Points is trained with surface containing rich geometric details, such that the trained model has enough expression ability for various shapes. Specifically, we extract deep local features on the points and construct neural fields through the local isomorphism between the 2D parametric domain and the 3D local patch. In the final, local neural fields are integrated together to form the global surface. Experimental results show that Neural Points has powerful representation ability and demonstrate excellent robustness and generalization ability. With Neural Points, we can resample point cloud with arbitrary resolutions, and it outperforms the state-of-the-art point cloud upsampling methods. Code is available at https://github.com/WanquanF/NeuralPoints. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.04148 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.html | CVPR 2022 | null |
Distribution Consistent Neural Architecture Search | Junyi Pan, Chong Sun, Yizhou Zhou, Ying Zhang, Chen Li | Recent progress on neural architecture search (NAS) has demonstrated exciting results on automating deep network architecture designs. In order to overcome the unaffordable complexity of training each candidate architecture from scratch, the state-of-the-art one-shot NAS approaches adopt a weight-sharing strategy to improve training efficiency. Although the computational cost is greatly reduced, such one-shot process introduces a severe weight coupling problem that largely degrades the evaluation accuracy of each candidate. The existing approaches often address the problem by shrinking the search space, model distillation, or few-shot training. Instead, in this paper, we propose a novel distribution consistent one-shot neural architecture search algorithm. We first theoretically investigate how the weight coupling problem affects the network searching performance from a parameter distribution perspective, and then propose a novel supernet training strategy with a Distribution Consistent Constraint that can provide a good measurement for the extent to which two architectures can share weights. Our strategy optimizes the supernet through iteratively inferring network weights and corresponding local sharing states. Such joint optimization of supernet's weights and topologies can diminish the discrepancy between the weights inherited from the supernet and the ones that are trained with a stand-alone model. As a result, it enables a more accurate model evaluation phase and leads to a better searching performance. We conduct extensive experiments on benchmark datasets with multiple searching spaces. The resulting architecture achieves superior performance over the current state-of-the-art NAS algorithms with comparable search costs, which demonstrates the efficacy of our approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Video-Text Representation Learning via Differentiable Weak Temporal Alignment | Dohwan Ko, Joonmyung Choi, Juyeon Ko, Shinyeong Noh, Kyoung-Woon On, Eun-Sol Kim, Hyunwoo J. Kim | Learning generic joint representations for video and text by a supervised method requires a prohibitively substantial amount of manually annotated video datasets. As a practical alternative, a large-scale but uncurated and narrated video dataset, HowTo100M, has recently been introduced. But it is still challenging to learn joint embeddings of video and text in a self-supervised manner, due to its ambiguity and non-sequential alignment. In this paper, we propose a novel multi-modal self-supervised framework Video-Text Temporally Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant information from noisy and weakly correlated data using a variant of Dynamic Time Warping (DTW). We observe that the standard DTW inherently cannot handle weakly correlated data and only considers the globally optimal alignment path. To address these problems, we develop a differentiable DTW which also reflects local information with weak temporal alignment. Moreover, our proposed model applies a contrastive learning scheme to learn feature representations on weakly correlated data. Our extensive experiments demonstrate that VT-TWINS attains significant improvements in multi-modal representation learning and outperforms various challenging downstream tasks. Code is available at https://github.com/mlvlab/VT-TWINS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ko_Video-Text_Representation_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16784 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Bi-Directional Object-Context Prioritization Learning for Saliency Ranking | Xin Tian, Ke Xu, Xin Yang, Lin Du, Baocai Yin, Rynson W.H. Lau | The saliency ranking task is recently proposed to study the visual behavior that humans would typically shift their attention over different objects of a scene based on their degrees of saliency. Existing approaches focus on learning either object-object or object-scene relations. Such a strategy follows the idea of object-based attention in Psychology, but it tends to favor those objects with strong semantics (e.g., humans), resulting in unrealistic saliency ranking. We observe that spatial attention works concurrently with object-based attention in the human visual recognition system. During the recognition process, the human spatial attention mechanism would move, engage, and disengage from region to region (i.e., context to context). This inspires us to model the region-level interactions, in addition to the object-level reasoning, for saliency ranking. To this end, we propose a novel bi-directional method to unify spatial attention and object-based attention for saliency ranking. Our model includes two novel modules: (1) a selective object saliency (SOS) module that models object-based attention via inferring the semantic representation of the salient object, and (2) an object-context-object relation (OCOR) module that allocates saliency ranks to objects by jointly modeling the object-context and context-object interactions of the salient objects. Extensive experiments show that our approach outperforms existing state-of-the-art methods. Code and pretrained model are available at https://github.com/GrassBro/OCOR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tian_Bi-Directional_Object-Context_Prioritization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.html | CVPR 2022 | null |
FreeSOLO: Learning To Segment Objects Without Annotations | Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, Jose M. Alvarez | Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and segmentation masks for learning. In this work, we propose a fully unsupervised learning method that learns class-agnostic instance segmentation without any annotations. We present FreeSOLO, a self-supervised instance segmentation framework built on top of the simple instance segmentation method SOLO. Our method also presents a novel localization-aware pre-training framework, where objects can be discovered from complicated scenes in an unsupervised manner. FreeSOLO achieves 9.8% AP50 on the challenging COCO dataset, which even outperforms several segmentation proposal methods that use manual annotations. For the first time, we demonstrate unsupervised class-agnostic instance segmentation successfully. FreeSOLO's box localization significantly outperforms state-of-the-art unsupervised object detection/discovery methods, with about 100% relative improvements in COCO AP. FreeSOLO further demonstrates superiority as a strong pre-training method, outperforming state-of-the-art self-supervised pre-training methods by +9.8% AP when fine-tuning instance segmentation with only 5% COCO masks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_FreeSOLO_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.12181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.html | CVPR 2022 | null |
What Do Navigation Agents Learn About Their Environment? | Kshitij Dwivedi, Gemma Roig, Aniruddha Kembhavi, Roozbeh Mottaghi | Today's state of the art visual navigation agents typically consist of large deep learning architectures trained end to end. Such models offer little to no interpretability about the skills learned by the agent or the actions taken by it in response to its environment. While past works have explored interpreting deep learning models, little attention has been devoted to interpreting embodied AI systems, which often involve reasoning about the structure of the environment, target characteristics and the outcome of one's actions. In this paper, we introduce the Interpretability System for Embodied agEnts (iSEE) for Point Goal (PointNav) and Object Goal (ObjectNav) navigation models. We use iSEE to probe the dynamic representations produced by PointNav and ObjectNav agents for the presence of information about their agents location and actions, as well as the environment. We demonstrate interesting insights about navigation agents using iSEE, including the ability to encode reachable locations (to avoid obstacles), visibility of the target, progress from the initial spawn location as well as the dramatic effect on the behaviors of agents when we mask out critical individual neurons. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dwivedi_What_Do_Navigation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.html | CVPR 2022 | null |
Progressive Minimal Path Method With Embedded CNN | Wei Liao | We propose Path-CNN, a method for the segmentation of centerlines of tubular structures by embedding convolutional neural networks (CNNs) into the progressive minimal path method. Minimal path methods are widely used for topology-aware centerline segmentation, but usually these methods rely on weak, hand-tuned image features. In contrast, CNNs use strong image features which are learned automatically from images. But CNNs usually do not take the topology of the results into account, and often require a large amount of annotations for training. We integrate CNNs into the minimal path method, so that both techniques benefit from each other: CNNs employ learned image features to improve the determination of minimal paths, while the minimal path method ensures the correct topology of the segmented centerlines, provides strong geometric priors to increase the performance of CNNs, and reduces the amount of annotations for the training of CNNs significantly. Our method has lower hardware requirements than many recent methods. Qualitative and quantitative comparison with other methods shows that Path-CNN achieves better performance, especially when dealing with tubular structures with complex shapes in challenging environments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.00944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.html | CVPR 2022 | null |
FIFO: Learning Fog-Invariant Features for Foggy Scene Segmentation | Sohyun Lee, Taeyoung Son, Suha Kwak | Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images with different fog conditions in neural style spaces of a segmentation model. In particular, since the neural style of an image is in general affected by other factors as well as fog, we introduce a fog-pass filter module that learns to extract a fog-relevant factor from the style. Optimizing the fog-pass filter and the segmentation model alternately gradually closes the style gap between different fog conditions and allows to learn fog-invariant features in consequence. Our method substantially outperforms previous work on three real foggy image datasets. Moreover, it improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_FIFO_Learning_Fog-Invariant_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01587 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
3D Human Tongue Reconstruction From Single "In-the-Wild" Images | Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou | 3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination. Since the introduction of the 3D Morphable Model in the late 90's, we witnessed an explosion of research aiming at particularly tackling this task. Nevertheless, despite the increasing level of detail in the 3D face reconstructions from single images mainly attributed to deep learning advances, finer and highly deformable components of the face such as the tongue are still absent from all 3D face models in the literature, although being very important for the realness of the 3D avatar representations. In this work we present the first, to the best of our knowledge, end-to-end trainable pipeline that accurately reconstructs the 3D face together with the tongue. Moreover, we make this pipeline robust in "in-the-wild" images by introducing a novel GAN method tailored for 3D tongue surface generation. Finally, we make publicly available to the community the first diverse tongue dataset, consisting of 1,800 raw scans of 700 individuals varying in gender, age, and ethnicity backgrounds. As we demonstrate in an extensive series of quantitative as well as qualitative experiments, our model proves to be robust and realistically captures the 3D tongue structure, even in adverse "in-the-wild" conditions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ploumpis_3D_Human_Tongue_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.12302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Enhancing Adversarial Robustness for Deep Metric Learning | Mo Zhou, Vishal M. Patel | Owing to security implications of adversarial vulnerability, adversarial robustness of deep metric learning models has to be improved. In order to avoid model collapse due to excessively hard examples, the existing defenses dismiss the min-max adversarial training, but instead learn from a weak adversary inefficiently. Conversely, we propose Hardness Manipulation to efficiently perturb the training triplet till a specified level of hardness for adversarial training, according to a harder benign triplet or a pseudo-hardness function. It is flexible since regular training and min-max adversarial training are its boundary cases. Besides, Gradual Adversary, a family of pseudo-hardness functions is proposed to gradually increase the specified hardness level during training for a better balance between performance and robustness. Additionally, an Intra-Class Structure loss term among benign and adversarial examples further improves model robustness and efficiency. Comprehensive experimental results suggest that the proposed method, although simple in its form, overwhelmingly outperforms the state-of-the-art defenses in terms of robustness, training efficiency, as well as performance on benign examples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Enhancing_Adversarial_Robustness_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation | Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, David Z. Pan | Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly designed for image classification that generate single-scale low-resolution representations, which makes dense prediction tasks such as semantic segmentation challenging for ViTs. Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs. We balance the model performance and efficiency of HRViT by various branch-block co-optimization techniques. Specifically, we explore heterogeneous branch designs, reduce the redundancy in linear layers, and augment the attention block with enhanced expressiveness. Those approaches enabled \ours to push the Pareto frontier of performance and efficiency on semantic segmentation to a new level, as our evaluation results on ADE20K and Cityscapes show. HRViT achieves 50.20% mIoU on ADE20K and 83.16% mIoU on Cityscapes for semantic segmentation tasks, surpassing state-of-the-art MiT and CSWin backbones with an average of +1.78 mIoU improvement, 28% parameter reduction, and 21% FLOPs reduction, demonstrating the potential of HRViT as a strong vision backbone for semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_Multi-Scale_High-Resolution_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.01236 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Lite-MDETR: A Lightweight Multi-Modal Detector | Qian Lou, Yen-Chang Hsu, Burak Uzkent, Ting Hua, Yilin Shen, Hongxia Jin | Recent multi-modal detectors based on transformers and modality encoders have successfully achieved impressive results on end-to-end visual object detection conditioned on a raw text query. However, they require a large model size and an enormous amount of computations to achieve high performance, which makes it difficult to deploy mobile applications that are limited by tight hardware resources. In this paper, we present a Lightweight modulated detector, Lite-MDETR, to facilitate efficient end-to-end multi-modal understanding on mobile devices. The key primitive is that Dictionary-Lookup-Transformormations (DLT) is proposed to replace Linear Transformation (LT) in multi-modal detectors where each weight in Linear Transformation (LT) is approximately factorized into a smaller dictionary, index, and coefficient. This way, the enormous linear projection with weights is converted into lite linear projection with dictionaries, a few lookups and scalings with indices and coefficients. DLT can be directly applied to pre-trained detectors, removing the need to perform expensive training from scratch. To tackle the challenging training of DLT due to the non-differentiable index, we convert the index and coefficient into a sparse matrix, train this sparse matrix during the fine-tuning phase, and recover it back to index and coefficient during the inference phase. Extensive experiments on several tasks such as phrase grounding, referring expression comprehension and segmentation show that our Lite-MDETR achieves similar detection accuracy to the prior multi-modal detectors with ~ 4.1xmodel size reduction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.html | CVPR 2022 | null |
CoordGAN: Self-Supervised Dense Correspondences Emerge From GANs | Jiteng Mu, Shalini De Mello, Zhiding Yu, Nuno Vasconcelos, Xiaolong Wang, Jan Kautz, Sifei Liu | Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, expression, layout, etc. While this indicates that GANs implicitly learn pixel-level correspondences across images, few studies explored how to extract them explicitly. In this work, we introduce Coordinate GAN (CoordGAN), a structure-texture disentangled GAN that learns a dense correspondence map for each generated image. We represent the correspondence maps of different images as warped coordinate frames transformed from a canonical coordinate frame, i.e., the correspondence map, which describes the structure (e.g., the shape of a face), is controlled via a transformation. Hence, finding correspondences boils down to locating the same coordinate in different correspondence maps. In CoordGAN, we sample a transformation to represent the structure of a synthesized instance, while an independent texture branch is responsible for rendering appearance details orthogonal to the structure. Our approach can also extract dense correspondence maps for real images by adding an encoder on top of the generator. We quantitatively demonstrate the quality of the learned dense correspondences through segmentation mask transfer on multiple datasets. We also show that the proposed generator achieves better structure and texture disentanglement compared to existing approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mu_CoordGAN_Self-Supervised_Dense_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16521 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.html | CVPR 2022 | null |
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation | Yutong Chen, Fangyun Wei, Xiao Sun, Zhirong Wu, Stephen Lin | This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken language translation models. Data is thus a bottleneck for training effective sign language translation models. To mitigate this problem, we propose to progressively pretrain the model from general-domain datasets that include a large amount of external supervision to within-domain datasets. Concretely, we pretrain the sign-to-gloss visual network on the general domain of human actions and the within-domain of a sign-to-gloss dataset, and pretrain the gloss-to-text translation network on the general domain of a multilingual corpus and the within-domain of a gloss-to-text corpus. The joint model is fine-tuned with an additional module named the visual-language mapper that connects the two networks. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning. With its simplicity and strong performance, this approach can serve as a solid baseline for future research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_A_Simple_Multi-Modality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Visual Representation Learning by Online Constrained K-Means | Qi Qian, Yuanhong Xu, Juhua Hu, Hao Li, Rong Jin | Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering since prevalent clustering methods (e.g., k-means) have to run in a batch mode. Besides, there can be a trivial solution consisting of a dominating cluster. To address these challenges, we first investigate the objective of clustering-based representation learning. Based on this, we propose a novel clustering-based pretext task with online Constrained K-means (CoKe). Compared with the balanced clustering that each cluster has exactly the same size, we only constrain the minimal size of each cluster to flexibly capture the inherent data structure. More importantly, our online assignment method has a theoretical guarantee to approach the global optimum. By decoupling clustering and discrimination, CoKe can achieve competitive performance when optimizing with only a single view from each instance. Extensive experiments on ImageNet and other benchmark data sets verify both the efficacy and efficiency of our proposal. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qian_Unsupervised_Visual_Representation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Point Light Fields | Julian Ost, Issam Laradji, Alejandro Newell, Yuval Bahat, Felix Heide | We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining differentiable volume rendering with learned implicit density representations has made it possible to synthesize photo-realistic images for novel views of small scenes. As neural volumetric rendering methods require dense sampling of the underlying functional scene representation, at hundreds of samples along a ray cast through the volume, they are fundamentally limited to small scenes with the same objects projected to hundreds of training views. Promoting sparse point clouds to neural implicit light fields allows us to represent large scenes effectively with only a single radiance evaluation per ray. These point light fields are as a function of the ray direction, and local point feature neighborhood, allowing us to interpolate the light field conditioned training images without dense object coverage and parallax. We assess the proposed method for novel view synthesis on large driving scenarios, where we synthesize realistic unseen views that existing implicit approaches fail to represent. We validate that Neural Point Light Fields make it possible to predict videos along unseen trajectories previously only feasible to generate by explicitly modeling the scene. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ost_Neural_Point_Light_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.01473 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
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