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Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification | Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, Yi Shan | Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Dual_Cross-Attention_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization | Canjie Luo, Lianwen Jin, Jingdong Chen | Recently self-supervised representation learning has drawn considerable attention from the scene text recognition community. Different from previous studies using contrastive learning, we tackle the issue from an alternative perspective, i.e., by formulating the representation learning scheme in a generative manner. Typically, the neighboring image patches among one text line tend to have similar styles, including the strokes, textures, colors, etc. Motivated by this common sense, we augment one image patch and use its neighboring patch as guidance to recover itself. Specifically, we propose a Similarity-Aware Normalization (SimAN) module to identify the different patterns and align the corresponding styles from the guiding patch. In this way, the network gains representation capability for distinguishing complex patterns such as messy strokes and cluttered backgrounds. Experiments show that the proposed SimAN significantly improves the representation quality and achieves promising performance. Moreover, we surprisingly find that our self-supervised generative network has impressive potential for data synthesis, text image editing, and font interpolation, which suggests that the proposed SimAN has a wide range of practical applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_SimAN_Exploring_Self-Supervised_Representation_Learning_of_Scene_Text_via_Similarity-Aware_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_SimAN_Exploring_Self-Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10492 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_SimAN_Exploring_Self-Supervised_Representation_Learning_of_Scene_Text_via_Similarity-Aware_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_SimAN_Exploring_Self-Supervised_Representation_Learning_of_Scene_Text_via_Similarity-Aware_CVPR_2022_paper.html | CVPR 2022 | null |
GASP, a Generalized Framework for Agglomerative Clustering of Signed Graphs and Its Application to Instance Segmentation | Alberto Bailoni, Constantin Pape, Nathan Hütsch, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht | We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many combinations of different linkage criteria and cannot-link constraints. We prove the equivalence of existing clustering methods to some of those combinations and introduce new algorithms for combinations that have not been studied before. We study both theoretical and empirical properties of these combinations and prove that some of these define an ultrametric on the graph. We conduct a systematic comparison of various instantiations of GASP on a large variety of both synthetic and existing signed clustering problems, in terms of accuracy but also efficiency and robustness to noise. Lastly, we show that some of the algorithms included in our framework, when combined with the predictions from a CNN model, result in a simple bottom-up instance segmentation pipeline. Going all the way from pixels to final segments with a simple procedure, we achieve state-of-the-art accuracy on the CREMI 2016 EM segmentation benchmark without requiring domain-specific superpixels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bailoni_GASP_a_Generalized_Framework_for_Agglomerative_Clustering_of_Signed_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bailoni_GASP_a_Generalized_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bailoni_GASP_a_Generalized_Framework_for_Agglomerative_Clustering_of_Signed_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bailoni_GASP_a_Generalized_Framework_for_Agglomerative_Clustering_of_Signed_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
Estimating Example Difficulty Using Variance of Gradients | Chirag Agarwal, Daniel D'souza, Sara Hooker | In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection, and provides interpretability into model behavior. In this work, we propose Variance of Gradients (VoG) as a valuable and efficient metric to rank data by difficulty and to surface a tractable subset of the most challenging examples for human-in-the-loop auditing. We show that data points with high VoG scores are far more difficult for the model to learn and over-index on corrupted or memorized examples. Further, restricting the evaluation to the test set instances with the lowest VoG improves the model's generalization performance. Finally, we show that VoG is a valuable and efficient ranking for out-of-distribution detection | https://openaccess.thecvf.com/content/CVPR2022/papers/Agarwal_Estimating_Example_Difficulty_Using_Variance_of_Gradients_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Agarwal_Estimating_Example_Difficulty_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2008.11600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Agarwal_Estimating_Example_Difficulty_Using_Variance_of_Gradients_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Agarwal_Estimating_Example_Difficulty_Using_Variance_of_Gradients_CVPR_2022_paper.html | CVPR 2022 | null |
One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching | Khoa D. Doan, Peng Yang, Ping Li | Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. Hashing aims to learn a binary-output function that maps an image to a binary vector. For optimal retrieval performance, producing balanced hash codes with low-quantization error to bridge the gap between the learning stage's continuous relaxation and the inference stage's discrete quantization is important. However, in the existing deep supervised hashing methods, coding balance and low-quantization error are difficult to achieve and involve several losses. We argue that this is because the existing quantization approaches in these methods are heuristically constructed and not effective to achieve these objectives. This paper considers an alternative approach to learning the quantization constraints. The task of learning balanced codes with low quantization error is re-formulated as matching the learned distribution of the continuous codes to a pre-defined discrete, uniform distribution. This is equivalent to minimizing the distance between two distributions. We then propose a computationally efficient distributional distance by leveraging the discrete property of the hash functions. This distributional distance is a valid distance and enjoys lower time and sample complexities. The proposed single-loss quantization objective can be integrated into any existing supervised hashing method to improve code balance and quantization error. Experiments confirm that the proposed approach substantially improves the performance of several representative hashing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Doan_One_Loss_for_Quantization_Deep_Hashing_With_Discrete_Wasserstein_Distributional_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Doan_One_Loss_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.15721 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Doan_One_Loss_for_Quantization_Deep_Hashing_With_Discrete_Wasserstein_Distributional_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Doan_One_Loss_for_Quantization_Deep_Hashing_With_Discrete_Wasserstein_Distributional_CVPR_2022_paper.html | CVPR 2022 | null |
Pixel Screening Based Intermediate Correction for Blind Deblurring | Meina Zhang, Yingying Fang, Guoxi Ni, Tieyong Zeng | Blind deblurring has attracted much interest with its wide applications in reality. The blind deblurring problem is usually solved by estimating the intermediate kernel and the intermediate image alternatively, which will finally converge to the blurring kernel of the observed image. Numerous works have been proposed to obtain intermediate images with fewer undesirable artifacts by designing delicate regularization on the latent solution. However, these methods still fail while dealing with images containing saturations and large blurs. To address this problem, we propose an intermediate image correction method which utilizes Bayes posterior estimation to screen through the intermediate image and exclude those unfavorable pixels to reduce their influence for kernel estimation. Extensive experiments have proved that the proposed method can effectively improve the accuracy of the final derived kernel against the state-of-the-art methods on benchmark datasets by both quantitative and qualitative comparisons. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Pixel_Screening_Based_Intermediate_Correction_for_Blind_Deblurring_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Pixel_Screening_Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Pixel_Screening_Based_Intermediate_Correction_for_Blind_Deblurring_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Pixel_Screening_Based_Intermediate_Correction_for_Blind_Deblurring_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast | Ye Du, Zehua Fu, Qingjie Liu, Yunhong Wang | Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation. In this study, we propose weakly-supervised pixel-to-prototype contrast that can provide pixel-level supervisory signals to narrow the gap. Guided by two intuitive priors, our method is executed across different views and within per single view of an image, aiming to impose cross-view feature semantic consistency regularization and facilitate intra(inter)-class compactness(dispersion) of the feature space. Our method can be seamlessly incorporated into existing WSSS models without any changes to the base networks and does not incur any extra inference burden. Extensive experiments manifest that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness. Specifically, built on top of SEAM, we improve the initial seed mIoU on PASCAL VOC 2012 from 55.4% to 61.5%. Moreover, armed with our method, we increase the segmentation mIoU of EPS from 70.8% to 73.6%, achieving new state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Weakly_Supervised_Semantic_Segmentation_by_Pixel-to-Prototype_Contrast_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2110.07110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Weakly_Supervised_Semantic_Segmentation_by_Pixel-to-Prototype_Contrast_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Weakly_Supervised_Semantic_Segmentation_by_Pixel-to-Prototype_Contrast_CVPR_2022_paper.html | CVPR 2022 | null |
Controllable Animation of Fluid Elements in Still Images | Aniruddha Mahapatra, Kuldeep Kulkarni | We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements like water, smoke, fire, which have the properties of repeating textures and continuous fluid motion. Taking inspiration from prior works, we represent the motion of such fluid elements in the image in the form of a constant 2D optical flow map. To this end, we allow the user to provide any number of arrow directions and their associated speeds along with a mask of the regions the user wants to animate. The user-provided input arrow directions, their corresponding speed values, and the mask are then converted into a dense flow map representing a constant optical flow map (F_D). We observe that F_D, obtained using simple exponential operations can closely approximate the plausible motion of elements in the image. We further refine computed dense optical flow map F_D using a generative-adversarial network (GAN) to obtain a more realistic flow map. We devise a novel UNet based architecture to autoregressively generate future frames using the refined optical flow map by forward-warping the input image features at different resolutions. We conduct extensive experiments on a publicly available dataset and show that our method is superior to the baselines in terms of qualitative and quantitative metrics. In addition, we show the qualitative animations of the objects in directions that did not exist in the training set and provide a way to synthesize videos that otherwise would not exist in the real world. Project url: https://controllable-cinemagraphs.github.io/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.03051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Holocurtains: Programming Light Curtains via Binary Holography | Dorian Chan, Srinivasa G. Narasimhan, Matthew O'Toole | Light curtain systems are designed for detecting the presence of objects within a user-defined 3D region of space, which has many applications across vision and robotics. However, the shape of light curtains have so far been limited to ruled surfaces, i.e., surfaces composed of straight lines. In this work, we propose Holocurtains: a light-efficient approach to producing light curtains of arbitrary shape. The key idea is to synchronize a rolling-shutter camera with a 2D holographic projector, which steers (rather than block) light to generate bright structured light patterns. Our prototype projector uses a binary digital micromirror device (DMD) to generate the holographic interference patterns at high speeds. Our system produces 3D light curtains that cannot be achieved with traditional light curtain setups and thus enables all-new applications, including the ability to simultaneously capture multiple light curtains in a single frame, detect subtle changes in scene geometry, and transform any 3D surface into an optical touch interface. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Holocurtains_Programming_Light_Curtains_via_Binary_Holography_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_Holocurtains_Programming_Light_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Holocurtains_Programming_Light_Curtains_via_Binary_Holography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Holocurtains_Programming_Light_Curtains_via_Binary_Holography_CVPR_2022_paper.html | CVPR 2022 | null |
Recurrent Dynamic Embedding for Video Object Segmentation | Mingxing Li, Li Hu, Zhiwei Xiong, Bang Zhang, Pan Pan, Dong Liu | Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably introduces lots of noise, which is not conducive to reading the most important information from the memory bank. In this paper, we propose a Recurrent Dynamic Embedding (RDE) to build a memory bank of constant size. Specifically, we explicitly generate and update RDE by the proposed Spatio-temporal Aggregation Module (SAM), which exploits the cue of historical information. To avoid error accumulation owing to the recurrent usage of SAM, we propose an unbiased guidance loss during the training stage, which makes SAM more robust in long videos. Moreover, the predicted masks in the memory bank are inaccurate due to the inaccurate network inference, which affects the segmentation of the query frame. To address this problem, we design a novel self-correction strategy so that the network can repair the embeddings of masks with different qualities in the memory bank. Extensive experiments show our method achieves the best tradeoff between performance and speed. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Recurrent_Dynamic_Embedding_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Hierarchical Semantic Segmentation | Liulei Li, Tianfei Zhou, Wenguan Wang, Jianwu Li, Yi Yang | Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing work is often aware of flatten labels and predicts target classes exclusively for each pixel. In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise HSSN, a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the hierarchy information to regularize HSS network learning. To address i), HSSN directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), HSSN first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Further, with hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and PASCAL-Person-Part), with different class hierarchies, segmentation network architectures and backbones, showing the generalization and superiority of HSSN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Deep_Hierarchical_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Deep_Hierarchical_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Deep_Hierarchical_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Deep_Hierarchical_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
f-SfT: Shape-From-Template With a Physics-Based Deformation Model | Navami Kairanda, Edith Tretschk, Mohamed Elgharib, Christian Theobalt, Vladislav Golyanik | Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods. For the source code and data, see https://4dqv.mpi-inf.mpg.de/phi-SfT/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kairanda_f-SfT_Shape-From-Template_With_a_Physics-Based_Deformation_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kairanda_f-SfT_Shape-From-Template_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kairanda_f-SfT_Shape-From-Template_With_a_Physics-Based_Deformation_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kairanda_f-SfT_Shape-From-Template_With_a_Physics-Based_Deformation_Model_CVPR_2022_paper.html | CVPR 2022 | null |
Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism | Binbin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, Xiaodan Liang | Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual classification, the researches of continual object detection are impeded by the diverse sizes and numbers of objects in each image. Different from previous works that tune the whole network for all tasks, in this work, we present a simple and flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTing mechAnism (ROSETTA). Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. In this way, various knowledge can be successively memorized by storing their corresponding sub-model weights in this system. To make ROSETTA automatically determine which experience is available and useful, a prototypical task correlation guided Gating Diversity Controller (GDC) is introduced to adaptively adjust the diversity of gates for the new task based on class-specific prototypes. GDC module computes class-to-class correlation matrix to depict the cross-task correlation, and hereby activates more exclusive gates for the new task if a significant domain gap is observed. Comprehensive experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance on both task-based and class-based continual object detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Continual_Object_Detection_via_Prototypical_Task_Correlation_Guided_Gating_Mechanism_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Continual_Object_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03055 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Continual_Object_Detection_via_Prototypical_Task_Correlation_Guided_Gating_Mechanism_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Continual_Object_Detection_via_Prototypical_Task_Correlation_Guided_Gating_Mechanism_CVPR_2022_paper.html | CVPR 2022 | null |
DATA: Domain-Aware and Task-Aware Self-Supervised Learning | Qing Chang, Junran Peng, Lingxi Xie, Jiajun Sun, Haoran Yin, Qi Tian, Zhaoxiang Zhang | The paradigm of training models on massive data without label through self-supervised learning (SSL) and finetuning on many downstream tasks has become a trend recently. However, due to the high training costs and the unconsciousness of downstream usages, most self-supervised learning methods lack the capability to correspond to the diversities of downstream scenarios, as there are various data domains, latency constraints and etc. Neural architecture search (NAS) is one universally acknowledged fashion to conquer the issues above, but applying NAS on SSL seems impossible as there is no label or metric provided for judging model selection. In this paper, we present DATA, a simple yet effective NAS approach specialized for SSL that provides Domain-Aware and Task-Aware pre-training. Specifically, we (i) train a supernet which could be deemed as a set of millions of networks covering a wide range of model scales without any label, (ii) propose a flexible searching mechanism compatible with SSL that enables finding networks of different computation costs, for various downstream vision tasks and data domains without explicit metric provided. Instantiated With MoCov2, our method achieves promising results across a wide range of computation costs on downstream tasks, including image classification, object detection and semantic segmentation. DATA is orthogonal to most existing SSL methods and endows them the ability of customization on downstream needs. Extensive experiments on other SSL methods, including BYOL, ReSSL and DenseCL demonstrate the generalizability of the proposed method. Code would be made available soon. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_DATA_Domain-Aware_and_Task-Aware_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chang_DATA_Domain-Aware_and_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.09041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_DATA_Domain-Aware_and_Task-Aware_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_DATA_Domain-Aware_and_Task-Aware_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation | Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia | We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation. To leverage the unlabeled data to boost model performance, we present a novel Two-Way Inter-label Self-Training framework named TWIST. It exploits inherent correlations between semantic understanding and instance information of a scene. Specifically, we consider two kinds of pseudo labels for semantic- and instance-level supervision. Our key design is to provide object-level information for denoising pseudo labels and make use of their correlation for two-way mutual enhancement, thereby iteratively promoting the pseudo-label qualities. TWIST attains leading performance on both ScanNet and S3DIS, compared to recent 3D pre-training approaches, and can cooperate with them to further enhance performance, e.g., +4.4% AP50 on 1%-label ScanNet data-efficient benchmark. Code is available at https://github.com/dvlab-research/TWIST. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chu_TWIST_Two-Way_Inter-Label_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection From Point Clouds | Chenhang He, Ruihuang Li, Shuai Li, Lei Zhang | Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to apply the self-attention underlying transformer on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points into clusters of the same size, or perform convolutional self-attention on a discretized representation. However, the former results in stochastic point dropout, while the latter typically has narrow attention field. In this paper, we propose a novel voxel-based architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from point clouds by means of set-to-set translation. VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes. With the VSA module, VoxSeT can manage voxelized point clusters with arbitrary size in a wide range, and process them in parallel with linear complexity. The proposed VoxSeT integrates the high performance of transformer with the efficiency of voxel-based model, which can be used as a good alternative to the convolutional and point-based backbones. VoxSeT reports competitive results on the KITTI and Waymo detection benchmarks. The source code of VoxSeT will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Voxel_Set_Transformer_A_Set-to-Set_Approach_to_3D_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.10314 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Voxel_Set_Transformer_A_Set-to-Set_Approach_to_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Voxel_Set_Transformer_A_Set-to-Set_Approach_to_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Adaptive Warping for Real-World Rolling Shutter Correction | Mingdeng Cao, Zhihang Zhong, Jiahao Wang, Yinqiang Zheng, Yujiu Yang | This paper proposes a real-world rolling shutter (RS) correction dataset, BS-RSC, and a corresponding model to correct the RS frames in a distorted video. Mobile devices in the consumer market with CMOS-based sensors for video capture often result in rolling shutter effects when relative movements occur during the video acquisition process, calling for RS effect removal techniques. However, current state-of-the-art RS correction methods often fail to remove RS effects in real scenarios since the motions are various and hard to model. To address this issue, we propose a real-world RS correction dataset BS-RSC. Real distorted videos with corresponding ground truth are recorded simultaneously via a well-designed beam-splitter-based acquisition system. BS-RSC contains various motions of both camera and objects in dynamic scenes. Further, an RS correction model with adaptive warping is proposed. Our model can warp the learned RS features into global shutter counterparts adaptively with predicted multiple displacement fields. These warped features are aggregated and then reconstructed into high-quality global shutter frames in a coarse-to-fine strategy. Experimental results demonstrate the effectiveness of the proposed method, and our dataset can improve the model's ability to remove the RS effects in the real world. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_Learning_Adaptive_Warping_for_Real-World_Rolling_Shutter_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_Learning_Adaptive_Warping_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Learning_Adaptive_Warping_for_Real-World_Rolling_Shutter_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Learning_Adaptive_Warping_for_Real-World_Rolling_Shutter_Correction_CVPR_2022_paper.html | CVPR 2022 | null |
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning | Xiangyu Li, Xu Yang, Kun Wei, Cheng Deng, Muli Yang | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese Contrastive Embedding Network (SCEN) for unseen composition recognition. Considering the entanglement between state and object, we embed the visual feature into a Siamese Contrastive Space to capture prototypes of them separately, alleviating the interaction between state and object. In addition, we design a State Transition Module (STM) to increase the diversity of training compositions, improving the robustness of the recognition model. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three challenging benchmark datasets, including the recent proposed C-QGA dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions | Huaizu Jiang, Xiaojian Ma, Weili Nie, Zhiding Yu, Yuke Zhu, Anima Anandkumar | A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object interactions (HOIs) from natural images. It is inspired by two desirable characteristics from the classical Bongard problems (BPs): 1) few-shot concept learning, and 2) context-dependent reasoning. We carefully curate the few-shot instances with hard negatives, where positive and negative images only disagree on action labels, making mere recognition of object categories insufficient to complete our benchmarks. We also design multiple test sets to systematically study the generalization of visual learning models, where we vary the overlap of the HOI concepts between the training and test sets of few- shot instances, from partial to no overlaps. Bongard-HOI presents a substantial challenge to today's visual recognition models. The state-of-the-art HOI detection model achieves only 62% accuracy on few-shot binary prediction while even amateur human testers on MTurk have 91% accuracy. With the Bongard-HOI benchmark, we hope to further advance research efforts in visual reasoning, especially in holistic perception-reasoning systems and better representation learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Bongard-HOI_Benchmarking_Few-Shot_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.html | CVPR 2022 | null |
RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures | Chengjie Niu, Manyi Li, Kai Xu, Hao Zhang | We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each level of the tree corresponds to an assembly of shape parts, represented as implicit functions, to reconstruct the input shape. At each node of the tree, simultaneous feature decoding and shape decomposition are carried out by their respective feature and part decoders, with weight sharing across the same hierarchy level. As an implicit field decoder, the part decoder is designed to decompose a sub-shape, via a two-way branched reconstruction, where each branch predicts a set of parameters defining a Gaussian to serve as a local point distribution for shape reconstruction. With reconstruction losses accounted for at each hierarchy level and a decomposition loss at each node, our network training does not require any ground-truth segmentations, let alone hierarchies. Through extensive experiments and comparisons to state-of-the-art alternatives, we demonstrate the quality, consistency, and interpretability of hierarchical structural inference by RIM-Net. | https://openaccess.thecvf.com/content/CVPR2022/papers/Niu_RIM-Net_Recursive_Implicit_Fields_for_Unsupervised_Learning_of_Hierarchical_Shape_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Niu_RIM-Net_Recursive_Implicit_Fields_for_Unsupervised_Learning_of_Hierarchical_Shape_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Niu_RIM-Net_Recursive_Implicit_Fields_for_Unsupervised_Learning_of_Hierarchical_Shape_CVPR_2022_paper.html | CVPR 2022 | null |
Do Learned Representations Respect Causal Relationships? | Lan Wang, Vishnu Naresh Boddeti | Data often has many semantic attributes that are causally associated with each other. But do attribute-specific learned representations of data also respect the same causal relations? We answer this question in three steps. First, we introduce NCINet, an approach for observational causal discovery from high-dimensional data. It is trained purely on synthetically generated representations and can be applied to real representations, and is specifically designed to mitigate the domain gap between the two. Second, we apply NCINet to identify the causal relations between image representations of different pairs of attributes with known and unknown causal relations between the labels. For this purpose, we consider image representations learned for predicting attributes on the 3D Shapes, CelebA, and the CASIA-WebFace datasets, which we annotate with multiple multi-class attributes. Third, we analyze the effect on the underlying causal relation between learned representations induced by various design choices in representation learning. Our experiments indicate that (1) NCINet significantly outperforms existing observational causal discovery approaches for estimating the causal relation between pairs of random samples, both in the presence and absence of an unobserved confounder, (2) under controlled scenarios, learned representations can indeed satisfy the underlying causal relations between their respective labels, and (3) the causal relations are positively correlated with the predictive capability of the representations. Code and annotations are available at: https://github.com/human-analysis/causal-relations-between-representations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Do_Learned_Representations_Respect_Causal_Relationships_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Do_Learned_Representations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Do_Learned_Representations_Respect_Causal_Relationships_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Do_Learned_Representations_Respect_Causal_Relationships_CVPR_2022_paper.html | CVPR 2022 | null |
ZebraPose: Coarse To Fine Surface Encoding for 6DoF Object Pose Estimation | Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari | Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases. | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Su_ZebraPose_Coarse_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09418 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.html | CVPR 2022 | null |
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic | Yoad Tewel, Yoav Shalev, Idan Schwartz, Lior Wolf | Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to generate a descriptive text given an image at inference time, without any further training or tuning step. This is done by combining the visual-semantic model with a large language model, benefiting from the knowledge in both web-scale models. The resulting captions are much less restrictive than those obtained by supervised captioning methods. Moreover, as a zero-shot learning method, it is extremely flexible and we demonstrate its ability to perform image arithmetic in which the inputs can be either images or text and the output is a sentence. This enables novel high-level vision capabilities such as comparing two images or solving visual analogy tests. Our code is available at: https://github.com/YoadTew/zero-shot-image-to-text. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tewel_ZeroCap_Zero-Shot_Image-to-Text_Generation_for_Visual-Semantic_Arithmetic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tewel_ZeroCap_Zero-Shot_Image-to-Text_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14447 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tewel_ZeroCap_Zero-Shot_Image-to-Text_Generation_for_Visual-Semantic_Arithmetic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tewel_ZeroCap_Zero-Shot_Image-to-Text_Generation_for_Visual-Semantic_Arithmetic_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Affiliate: Mutual Centralized Learning for Few-Shot Classification | Yang Liu, Weifeng Zhang, Chao Xiang, Tu Zheng, Deng Cai, Xiaofei He | Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks, given only a few examples. To handle the limited-data in few-shot regimes, recent methods tend to collectively use a set of local features to densely represent an image instead of using a mixed global feature. They generally explore a unidirectional paradigm, e.g., find the nearest support feature for every query feature and aggregate these local matches for a joint classification. In this paper, we propose a novel Mutual Centralized Learning (MCL) to fully affiliate these two disjoint dense features sets in a bidirectional paradigm. We first associate each local feature with a particle that can bidirectionally random walk in a discrete feature space. To estimate the class probability, we propose the dense features' accessibility that measures the expected number of visits to the dense features of that class in a Markov process. We relate our method to learning a centrality on an affiliation network and demonstrate its capability to be plugged in existing methods by highlighting centralized local features. Experiments show that our method achieves the new state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_To_Affiliate_Mutual_Centralized_Learning_for_Few-Shot_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_To_Affiliate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.05517 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Affiliate_Mutual_Centralized_Learning_for_Few-Shot_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Affiliate_Mutual_Centralized_Learning_for_Few-Shot_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
CAPRI-Net: Learning Compact CAD Shapes With Adaptive Primitive Assembly | Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang | We introduce CAPRI-Net, a self-supervised neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Given an input 3D shape, our network reconstructs it by an assembly of quadric surface primitives via constructive solid geometry (CSG) operations. Without any ground-truth shape assemblies, our self-supervised network is trained with a reconstruction loss, leading to faithful 3D reconstructions with sharp edges and plausible CSG trees. While the parametric nature of CAD models does make them more predictable locally, at the shape level, there is much structural and topological variation, which presents a significant generalizability challenge to state-of-the-art neural models for 3D shapes. Our network addresses this challenge by adaptive training with respect to each test shape, with which we fine-tune the network that was pre-trained on a model collection. We evaluate our learning framework on both ShapeNet and ABC, the largest and most diverse CAD dataset to date, in terms of reconstruction quality, sharp edges, compactness, and interpretability, to demonstrate superiority over current alternatives for neural CAD reconstruction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_CAPRI-Net_Learning_Compact_CAD_Shapes_With_Adaptive_Primitive_Assembly_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_CAPRI-Net_Learning_Compact_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_CAPRI-Net_Learning_Compact_CAD_Shapes_With_Adaptive_Primitive_Assembly_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_CAPRI-Net_Learning_Compact_CAD_Shapes_With_Adaptive_Primitive_Assembly_CVPR_2022_paper.html | CVPR 2022 | null |
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | Chunnan Wang, Xiang Chen, Junzhe Wang, Hongzhi Wang | Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users. Besides, the existing works ignore Federated Learning (FL) scenarios, failing to make full use of distributed multi-source datasets with rich actual scenes to learn more a powerful TP model. In this paper, we make up for the above defects and propose ATPFL to help users federate multi-source trajectory datasets to automatically design and train a powerful TP model. In ATPFL, we build an effective TP search space by analyzing and summarizing the existing works. Then, based on the characters of this search space, we design a relation-sequence-aware search strategy, realizing the automatic design of the TP model. Finally, we find appropriate federated training methods to respectively support the TP model search and final model training under the FL framework, ensuring both the search efficiency and the final model performance. Extensive experimental results show that ATPFL can help users gain well-performed TP models, achieving better results than the existing TP models trained on the single-source dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning | Moslem Yazdanpanah, Aamer Abdul Rahman, Muawiz Chaudhary, Christian Desrosiers, Mohammad Havaei, Eugene Belilovsky, Samira Ebrahimi Kahou | Batch Normalization is a staple of computer vision models, including those employed in few-shot learning. Batch Normalization layers in convolutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters gamma and beta. These affine parameters were introduced to maintain the expressive powers of the model following normalization. While this hypothesis holds true for classification within the same domain, this work illustrates that these parameters are detrimental to downstream performance on common few-shot transfer tasks. This effect is studied with multiple methods on well-known benchmarks such as few-shot classification on miniImageNet and cross-domain few-shot learning (CD-FSL). Experiments reveal consistent performance improvements on CNNs with affine unaccompanied Batch Normalization layers; particularly in large domain-shift few-shot transfer settings. As opposed to common practices in few-shot transfer learning where the affine parameters are fixed during the adaptation phase, we show fine-tuning them can lead to improved performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Bridging the Gap Between Classification and Localization for Weakly Supervised Object Localization | Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, Sungroh Yoon | Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a localization map; however, a CAM identifies only the most discriminative parts of a target object rather than the entire object region. In this work, we find the gap between classification and localization in terms of the misalignment of the directions between an input feature and a class-specific weight. We demonstrate that the misalignment suppresses the activation of CAM in areas that are less discriminative but belong to the target object. To bridge the gap, we propose a method to align feature directions with a class-specific weight. The proposed method achieves a state-of-the-art localization performance on the CUB-200-2011 and ImageNet-1K benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Bridging_the_Gap_Between_Classification_and_Localization_for_Weakly_Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Bridging_the_Gap_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00220 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Bridging_the_Gap_Between_Classification_and_Localization_for_Weakly_Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Bridging_the_Gap_Between_Classification_and_Localization_for_Weakly_Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation | Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Dan Xu | This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic localization map, we investigate if the transformer model can also effectively capture class-specific attention for more discriminative object localization by learning multiple class tokens within the transformer. To this end, we propose a Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens. The proposed MCTformer can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens. We also propose to use a patch-level pairwise affinity, which is extracted from the patch-to-patch transformer attention, to further refine the localization maps. Moreover, the proposed framework is shown to fully complement the Class Activation Mapping (CAM) method, leading to remarkably superior WSSS results on the PASCAL VOC and MS COCO datasets. These results underline the importance of the class token for WSSS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Multi-Class_Token_Transformer_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Multi-Class_Token_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Multi-Class_Token_Transformer_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Multi-Class_Token_Transformer_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
3D Moments From Near-Duplicate Photos | Qianqian Wang, Zhengqi Li, David Salesin, Noah Snavely, Brian Curless, Janne Kontkanen | We introduce 3D Moments, a new computational photography effect. As input we take a pair of near-duplicate photos, i.e., photos of moving subjects from similar viewpoints, common in people's photo collections. As output, we produce a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D. To achieve this effect, we represent the scene as a pair of feature-based layered depth images augmented with scene flow. This representation enables motion interpolation along with independent control of the camera viewpoint. Our system produces photorealistic space-time videos with motion parallax and scene dynamics, while plausibly recovering regions occluded in the original views. We conduct extensive experiments demonstrating superior performance over baselines on public datasets and in-the-wild photos. Project page: https://3d-moments.github.io/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.06255 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.html | CVPR 2022 | null |
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization | Yabin Zhang, Minghan Li, Ruihuang Li, Kui Jia, Lei Zhang | Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature distribution, conventional feature distribution matching methods usually match the mean and standard deviation of features. However, the feature distributions of real-world data are usually much more complicated than Gaussian, which cannot be accurately matched by using only the first-order and second-order statistics, while it is computationally prohibitive to use high-order statistics for distribution matching. In this work, we, for the first time to our best knowledge, propose to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching (EHM) in the image feature space. Particularly, a fast EHM algorithm, named Sort-Matching, is employed to perform EFDM in a plug-and-play manner with minimal cost. The effectiveness of our proposed EFDM method is verified on a variety of AST and DG tasks, demonstrating new state-of-the-art results. Codes are available at https://github.com/YBZh/EFDM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exact_Feature_Distribution_Matching_for_Arbitrary_Style_Transfer_and_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Exact_Feature_Distribution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exact_Feature_Distribution_Matching_for_Arbitrary_Style_Transfer_and_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exact_Feature_Distribution_Matching_for_Arbitrary_Style_Transfer_and_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots | Zejin Wang, Jiazheng Liu, Guoqing Li, Hua Han | Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem. However, self-supervised denoising methods, especially blindspot-driven ones, suffer sizable information loss during input or network design. The absence of valuable information dramatically reduces the upper bound of denoising performance. In this paper, we propose a simple yet efficient approach called Blind2Unblind to overcome the information loss in blindspot-driven denoising methods. First, we introduce a global-aware mask mapper that enables global perception and accelerates training. The mask mapper samples all pixels at blind spots on denoised volumes and maps them to the same channel, allowing the loss function to optimize all blind spots at once. Second, we propose a re-visible loss to train the denoising network and make blind spots visible. The denoiser can learn directly from raw noise images without losing information or being trapped in identity mapping. We also theoretically analyze the convergence of the re-visible loss. Extensive experiments on synthetic and real-world datasets demonstrate the superior performance of our approach compared to previous work. Code is available at https://github.com/demonsjin/Blind2Unblind. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Blind2Unblind_Self-Supervised_Image_Denoising_With_Visible_Blind_Spots_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Blind2Unblind_Self-Supervised_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06967 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Blind2Unblind_Self-Supervised_Image_Denoising_With_Visible_Blind_Spots_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Blind2Unblind_Self-Supervised_Image_Denoising_With_Visible_Blind_Spots_CVPR_2022_paper.html | CVPR 2022 | null |
Balanced and Hierarchical Relation Learning for One-Shot Object Detection | Hanqing Yang, Sijia Cai, Hualian Sheng, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Yong Tang, Yu Zhang | Instance-level feature matching is significantly important to the success of modern one-shot object detectors. Recently, the methods based on the metric-learning paradigm have achieved an impressive process. Most of these works only measure the relations between query and target objects on a single level, resulting in suboptimal performance overall. In this paper, we introduce the balanced and hierarchical learning for our detector. The contributions are two-fold: firstly, a novel Instance-level Hierarchical Relation (IHR) module is proposed to encode the contrastive-level, salient-level, and attention-level relations simultaneously to enhance the query-relevant similarity representation. Secondly, we notice that the batch training of the IHR module is substantially hindered by the positive-negative sample imbalance in the one-shot scenario. We then introduce a simple but effective Ratio-Preserving Loss (RPL) to protect the learning of rare positive samples and suppress the effects of negative samples. Our loss can adjust the weight for each sample adaptively, ensuring the desired positive-negative ratio consistency and boosting query-related IHR learning. Extensive experiments show that our method outperforms the state-of-the-art method by 1.6% and 1.3% on PASCAL VOC and MS COCO datasets for unseen classes, respectively. The code will be available at https://github.com/hero-y/BHRL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Generative Pretraining for Multimodal Video Captioning | Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, Cordelia Schmid | Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled videos which can be effectively used for generative tasks such as multimodal video captioning. Unlike recent video-language pretraining frameworks, our framework trains both a multimodal video encoder and a sentence decoder jointly. To overcome the lack of captions in unlabelled videos, we leverage the future utterance as an additional text source and propose a bidirectional generation objective -- we generate future utterances given the present mulitmodal context, and also the present utterance given future observations. With this objective, we train an encoder-decoder model end-to-end to generate a caption from raw pixels and transcribed speech directly. Our model achieves state-of-the-art performance for multimodal video captioning on four standard benchmarks, as well as for other video understanding tasks such as generative and discriminative VideoQA, video retrieval and action classification. | https://openaccess.thecvf.com/content/CVPR2022/papers/Seo_End-to-End_Generative_Pretraining_for_Multimodal_Video_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Seo_End-to-End_Generative_Pretraining_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2201.08264 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_End-to-End_Generative_Pretraining_for_Multimodal_Video_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_End-to-End_Generative_Pretraining_for_Multimodal_Video_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts | Chongzhi Zhang, Mingyuan Zhang, Shanghang Zhang, Daisheng Jin, Qiang Zhou, Zhongang Cai, Haiyu Zhao, Xianglong Liu, Ziwei Liu | Recently, Vision Transformers have achieved impressive results on various Vision tasks. Yet, their generalization ability under different distribution shifts is poorly understood. In this work, we provide a comprehensive study on the out-of-distribution generalization of Vision Transformers. To support a systematic investigation, we first present a taxonomy of distribution shifts by categorizing them into five conceptual levels: corruption shift, background shift, texture shift, destruction shift, and style shift. Then we perform extensive evaluations of Vision Transformer variants under different levels of distribution shifts and compare their generalization ability with Convolutional Neural Network (CNN) models. Several important observations are obtained: 1) Vision Transformers generalize better than CNNs under multiple distribution shifts. With the same or less amount of parameters, Vision Transformers are ahead of corresponding CNNs by more than 5% in top-1 accuracy under most types of distribution shift. In particular, Vision Transformers lead by more than 10% under the corruption shifts. 2) larger Vision Transformers gradually narrow the in-distribution (ID) and out-of-distribution (OOD) performance gap. To further improve the generalization of Vision Transformers, we design the enhanced Vision Transformers through self-supervised learning, information theory, and adversarial learning. By investigating these three types of generalization-enhanced Transformers, we observe the gradient-sensitivity of Vision Transformers and design a smoother learning strategy to achieve a stable training process. With modified training schemes, we achieve improvements on performance towards out-of-distribution data by 4% from vanilla Vision Transformers. We comprehensively compare these three types of generalization-enhanced Vision Transformers with their corresponding CNN models and observe that: 1) For the enhanced model, larger Vision Transformers still benefit more from the out-of-distribution generalization. 2) generalization-enhanced Vision Transformers are more sensitive to the hyper-parameters than their corresponding CNN models. We hope our comprehensive study could shed light on the design of more generalizable learning systems. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.07617 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.html | CVPR 2022 | null |
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM | Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, Marc Pollefeys | Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust. Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality. Project page: https://pengsongyou.github.io/nice-slam | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_NICE-SLAM_Neural_Implicit_Scalable_Encoding_for_SLAM_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_NICE-SLAM_Neural_Implicit_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_NICE-SLAM_Neural_Implicit_Scalable_Encoding_for_SLAM_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_NICE-SLAM_Neural_Implicit_Scalable_Encoding_for_SLAM_CVPR_2022_paper.html | CVPR 2022 | null |
HyperDet3D: Learning a Scene-Conditioned 3D Object Detector | Yu Zheng, Yueqi Duan, Jiwen Lu, Jie Zhou, Qi Tian | A bathtub in a library, a sink in an office, a bed in a laundry room - the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects. In this paper, we propose HyperDet3D to explore scene-conditioned prior knowledge for 3D object detection. Existing methods strive for better representation of local elements and their relations without sceneconditioned knowledge, which may cause ambiguity merely based on the understanding of individual points and object candidates. Instead, HyperDet3D simultaneously learns scene-agnostic embeddings and scene-specific knowledge through scene-conditioned hypernetworks. More specifically, our HyperDet3D not only explores the sharable abstracts from various 3D scenes, but also adapts the detector to the given scene at test time. We propose a discriminative Multi-head Scene-specific Attention (MSA) module to dynamically control the layer parameters of the detector conditioned on the fusion of scene-conditioned knowledge. Our HyperDet3D achieves state-of-the-art results on the 3D object detection benchmark of the ScanNet and SUN RGB-D datasets. Moreover, through cross-dataset evaluation, we show the acquired scene-conditioned prior knowledge still takes effect when facing 3D scenes with domain gap. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_HyperDet3D_Learning_a_Scene-Conditioned_3D_Object_Detector_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_HyperDet3D_Learning_a_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05599 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_HyperDet3D_Learning_a_Scene-Conditioned_3D_Object_Detector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_HyperDet3D_Learning_a_Scene-Conditioned_3D_Object_Detector_CVPR_2022_paper.html | CVPR 2022 | null |
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion | Tianpei Gu, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie Zhou, Jiwen Lu | Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states. Unlike existing stochastic trajectory prediction methods which usually use a latent variable to represent multi-modality, we explicitly simulate the process of human motion variation from indeterminate to determinate. In this paper, we present a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID), in which we progressively discard indeterminacy from all the walkable areas until reaching the desired trajectory. This process is learned with a parameterized Markov chain conditioned by the observed trajectories. We can adjust the length of the chain to control the degree of indeterminacy and balance the diversity and determinacy of the predictions. Specifically, we encode the history behavior information and the social interactions as a state embedding and devise a Transformer-based diffusion model to capture the temporal dependencies of trajectories. Extensive experiments on the human trajectory prediction benchmarks including the Stanford Drone and ETH/UCY datasets demonstrate the superiority of our method. Code is available at https://github.com/gutianpei/MID. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Stochastic_Trajectory_Prediction_via_Motion_Indeterminacy_Diffusion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_Stochastic_Trajectory_Prediction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Stochastic_Trajectory_Prediction_via_Motion_Indeterminacy_Diffusion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Stochastic_Trajectory_Prediction_via_Motion_Indeterminacy_Diffusion_CVPR_2022_paper.html | CVPR 2022 | null |
CLRNet: Cross Layer Refinement Network for Lane Detection | Tu Zheng, Yifei Huang, Yang Liu, Wenjian Tang, Zheng Yang, Deng Cai, Xiaofei He | Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane detection, but it is still under-explored. In this work, we present Cross Layer Refinement Network (CLRNet) aiming at fully utilizing both high-level and low-level features in lane detection. In particular, it first detects lanes with high-level semantic features then performs refinement based on low-level features. In this way, we can exploit more contextual information to detect lanes while leveraging local detailed lane features to improve localization accuracy. We present ROIGather to gather global context, which further enhances the feature representation of lanes. In addition to our novel network design, we introduce Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy. Experiments demonstrate that the proposed method greatly outperforms the state-of-the-art lane detection approaches. Code is available at:https://github.com/Turoad/CLRNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_CLRNet_Cross_Layer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10350 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Modal Map Learning for Vision and Language Navigation | Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo, Soham Dan, Eleni Miltsakaki, Dan Roth, Kostas Daniilidis | We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric observations of the agent. In contrast to other works, our key insight is that the association between language and vision is stronger when it occurs in explicit spatial representations. In this work, we propose a cross-modal map learning model for vision-and-language navigation that first learns to predict the top-down semantics on an egocentric map for both observed and unobserved regions, and then predicts a path towards the goal as a set of waypoints. In both cases, the prediction is informed by the language through cross-modal attention mechanisms. We experimentally test the basic hypothesis that language-driven navigation can be solved given a map, and then show competitive results on the full VLN-CE benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Georgakis_Cross-Modal_Map_Learning_for_Vision_and_Language_Navigation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Georgakis_Cross-Modal_Map_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Georgakis_Cross-Modal_Map_Learning_for_Vision_and_Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Georgakis_Cross-Modal_Map_Learning_for_Vision_and_Language_Navigation_CVPR_2022_paper.html | CVPR 2022 | null |
Motion-Aware Contrastive Video Representation Learning via Foreground-Background Merging | Shuangrui Ding, Maomao Li, Tianyu Yang, Rui Qian, Haohang Xu, Qingyi Chen, Jue Wang, Hongkai Xiong | In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate video representation learning. When naively pulling two augmented views of a video closer, the model however tends to learn the common static background as a shortcut but fails to capture the motion information, a phenomenon dubbed as background bias. Such bias makes the model suffer from weak generalization ability, leading to worse performance on downstream tasks such as action recognition. To alleviate such bias, we propose Foreground-background Merging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others. Specifically, without any off-the-shelf detector, we extract the moving foreground out of background regions via the frame difference and color statistics, and shuffle the background regions among the videos. By leveraging the semantic consistency between the original clips and the fused ones, the model focuses more on the motion patterns and is debiased from the background shortcut. Extensive experiments demonstrate that FAME can effectively resist background cheating and thus achieve the state-of-the-art performance on downstream tasks across UCF101, HMDB51, and Diving48 datasets. The code and configurations are released at https://github.com/Mark12Ding/FAME. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Motion-Aware_Contrastive_Video_Representation_Learning_via_Foreground-Background_Merging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Motion-Aware_Contrastive_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.15130 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Motion-Aware_Contrastive_Video_Representation_Learning_via_Foreground-Background_Merging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Motion-Aware_Contrastive_Video_Representation_Learning_via_Foreground-Background_Merging_CVPR_2022_paper.html | CVPR 2022 | null |
Incremental Transformer Structure Enhanced Image Inpainting With Masking Positional Encoding | Qiaole Dong, Chenjie Cao, Yanwei Fu | Image inpainting has made significant advances in recent years. However, it is still challenging to recover corrupted images with both vivid textures and reasonable structures. Some specific methods can only tackle regular textures while losing holistic structures due to the limited receptive fields of convolutional neural networks (CNNs). On the other hand, attention-based models can learn better long-range dependency for the structure recovery, but they are limited by the heavy computation for inference with large image sizes. To address these issues, we propose to leverage an additional structure restorer to facilitate the image inpainting incrementally. The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space. Such a grayscale space is easy to be upsampled to larger scales to convey correct structural information. Our structure restorer can be integrated with other pretrained inpainting models efficiently with the zero-initialized residual addition. Furthermore, a masking positional encoding strategy is utilized to improve the performance of the proposed model with large irregular masks. Extensive experiments on various datasets validate the efficacy of our model compared with other competitors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Incremental_Transformer_Structure_Enhanced_Image_Inpainting_With_Masking_Positional_Encoding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Incremental_Transformer_Structure_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.00867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Incremental_Transformer_Structure_Enhanced_Image_Inpainting_With_Masking_Positional_Encoding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Incremental_Transformer_Structure_Enhanced_Image_Inpainting_With_Masking_Positional_Encoding_CVPR_2022_paper.html | CVPR 2022 | null |
Pointly-Supervised Instance Segmentation | Bowen Cheng, Omkar Parkhi, Alexander Kirillov | We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask supervision can be seamlessly trained with point-based supervision collected via our scheme. Remarkably, Mask R-CNN trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only 10 annotated random points per object achieves 94%-98% of its fully-supervised performance, setting a strong baseline for weakly-supervised instance segmentation. The new point annotation scheme is approximately 5 times faster than annotating full object masks, making high-quality instance segmentation more accessible in practice. Inspired by the point-based annotation form, we propose a modification to PointRend instance segmentation module. For each object, the new architecture, called Implicit PointRend, generates parameters for a function that makes the final point-level mask prediction. Implicit PointRend is more straightforward and uses a single point-level mask loss. Our experiments show that the new module is more suitable for the point-based supervision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.06404 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Modal Clinical Graph Transformer for Ophthalmic Report Generation | Mingjie Li, Wenjia Cai, Karin Verspoor, Shirui Pan, Xiaodan Liang, Xiaojun Chang | Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with prior clinical knowledge. This knowledge has been neglected in prior medical report generation methods. To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure. However, two major common Knowledge Noise (KN) issues may affect models' effectiveness. 1) Existing general biomedical knowledge bases such as the UMLS may not align meaningfully to the specific context and language of the report, limiting their utility for knowledge injection. 2) Incorporating too much knowledge may divert the visual features from their correct meaning. To overcome these limitations, we design an automatic information extraction scheme based on natural language processing to obtain clinical entities and relations directly from in-domain training reports. Given a set of ophthalmic images, our CGT first restores a sub-graph from the clinical graph and injects the restored triples into visual features. Then visible matrix is employed during the encoding procedure to limit the impact of knowledge. Finally, reports are predicted by the encoded cross-modal features via a Transformer decoder. Extensive experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods and achieve state-of-the-art performances. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Human-Object Interaction Detection via Disentangled Transformer | Desen Zhou, Zhichao Liu, Jian Wang, Leshan Wang, Tao Hu, Errui Ding, Jingdong Wang | Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions. Existing HOI transformers either adopt a single decoder for triplet prediction, or utilize two parallel decoders to detect individual objects and interactions separately, and compose triplets by a matching process. In contrast, we decouple the triplet prediction into human-object pair detection and interaction classification. Our main motivation is that detecting the human-object instances and classifying interactions accurately needs to learn representations that focus on different regions. To this end, we present Disentangled Transformer, where both encoder and decoder are disentangled to facilitate learning of two subtasks. To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder. Extensive experiments show that our method outperforms prior work on two public HOI benchmarks by a sizeable margin. Code will be available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Human-Object_Interaction_Detection_via_Disentangled_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Human-Object_Interaction_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.09290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Human-Object_Interaction_Detection_via_Disentangled_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Human-Object_Interaction_Detection_via_Disentangled_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
DINE: Domain Adaptation From Single and Multiple Black-Box Predictors | Jian Liang, Dapeng Hu, Jiashi Feng, Ran He | To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target). Despite impressive progress, prior methods always need to access the raw source data and develop data-dependent alignment approaches to recognize the target samples in a transductive learning manner, which may raise privacy concerns from source individuals. Several recent studies resort to an alternative solution by exploiting the well-trained white-box model from the source domain, yet, it may still leak the raw data via generative adversarial learning. This paper studies a practical and interesting setting for UDA, where only black-box source models (i.e., only network predictions are available) are provided during adaptation in the target domain. To solve this problem, we propose a new two-step knowledge adaptation framework called DIstill and fine-tuNE (DINE). Taking into consideration the target data structure, DINE first distills the knowledge from the source predictor to a customized target model, then fine-tunes the distilled model to further fit the target domain. Besides, neural networks are not required to be identical across domains in DINE, even allowing effective adaptation on a low-resource device. Empirical results on three UDA scenarios (i.e., single-source, multi-source, and partial-set) confirm that DINE achieves highly competitive performance compared to state-of-the-art data-dependent approaches. Code is available at https://github.com/tim-learn/DINE/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.01539 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper.html | CVPR 2022 | null |
LGT-Net: Indoor Panoramic Room Layout Estimation With Geometry-Aware Transformer Network | Zhigang Jiang, Zhongzheng Xiang, Jinhua Xu, Ming Zhao | 3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizon-depth along with room height can obtain omnidirectional-geometry awareness of room layout in both horizontal and vertical directions. In addition, we propose a planar-geometry aware loss function with normals and gradients of normals to supervise the planeness of walls and turning of corners. We propose an efficient network, LGT-Net, for room layout estimation, which contains a novel Transformer architecture called SWG-Transformer to model geometry relations. SWG-Transformer consists of (Shifted) Window Blocks and Global Blocks to combine the local and global geometry relations. Moreover, we design a novel relative position embedding of Transformer to enhance the spatial identification ability for the panorama. Experiments show that the proposed LGT-Net achieves better performance than current state-of-the-arts (SOTA) on benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_LGT-Net_Indoor_Panoramic_Room_Layout_Estimation_With_Geometry-Aware_Transformer_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_LGT-Net_Indoor_Panoramic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_LGT-Net_Indoor_Panoramic_Room_Layout_Estimation_With_Geometry-Aware_Transformer_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_LGT-Net_Indoor_Panoramic_Room_Layout_Estimation_With_Geometry-Aware_Transformer_Network_CVPR_2022_paper.html | CVPR 2022 | null |
CRIS: CLIP-Driven Referring Image Segmentation | Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, Tongliang Liu | Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer the language/vision knowledge from pretrained models, ignoring the multi-modal corresponding information. Inspired by the recent advance in Contrastive Language-Image Pretraining (CLIP), in this paper, we propose an end-to-end CLIP-Driven Referring Image Segmentation framework (CRIS). To transfer the multi-modal knowledge effectively, CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. More specifically, we design a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities. In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances. The experimental results on three benchmark datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art performance without any post-processing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.15174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-View Mesh Reconstruction With Neural Deferred Shading | Markus Worchel, Rodrigo Diaz, Weiwen Hu, Oliver Schreer, Ingo Feldmann, Peter Eisert | We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. State-of-the-art methods use both neural surface representations and neural rendering. While flexible, neural surface representations are a significant bottleneck in optimization runtime. Instead, we represent surfaces as triangle meshes and build a differentiable rendering pipeline around triangle rasterization and neural shading. The renderer is used in a gradient descent optimization where both a triangle mesh and a neural shader are jointly optimized to reproduce the multi-view images. We evaluate our method on a public 3D reconstruction dataset and show that it can match the reconstruction accuracy of traditional baselines and neural approaches while surpassing them in optimization runtime. Additionally, we investigate the shader and find that it learns an interpretable representation of appearance, enabling applications such as 3D material editing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Worchel_Multi-View_Mesh_Reconstruction_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.html | CVPR 2022 | null |
CVF-SID: Cyclic Multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise From Image | Reyhaneh Neshatavar, Mohsen Yavartanoo, Sanghyun Son, Kyoung Mu Lee | Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs for each specific scenario is complicated and costly in practice. Consequently, applying a conventional supervised denoising network on in-the-wild noisy inputs is not straightforward. Although several studies have challenged this problem without strong supervision, they rely on less practical assumptions and cannot be applied to practical situations directly. To address the aforementioned challenges, we propose a novel and powerful self-supervised denoising method called CVF-SID based on a Cyclic multi-Variate Function (CVF) module and a self-supervised image disentangling (SID) framework. The CVF module can output multiple decomposed variables of the input and take a combination of the outputs back as an input in a cyclic manner. Our CVF-SID can disentangle a clean image and noise maps from the input by leveraging various self-supervised loss terms. Unlike several methods that only consider the signal-independent noise models, we also deal with signal-dependent noise components for real-world applications. Furthermore, we do not rely on any prior assumptions about the underlying noise distribution, making CVF-SID more generalizable toward realistic noise. Extensive experiments on real-world datasets show that CVF-SID achieves state-of-the-art self-supervised image denoising performance and is comparable to other existing approaches. The code is publicly available from this link. | https://openaccess.thecvf.com/content/CVPR2022/papers/Neshatavar_CVF-SID_Cyclic_Multi-Variate_Function_for_Self-Supervised_Image_Denoising_by_Disentangling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Neshatavar_CVF-SID_Cyclic_Multi-Variate_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Neshatavar_CVF-SID_Cyclic_Multi-Variate_Function_for_Self-Supervised_Image_Denoising_by_Disentangling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Neshatavar_CVF-SID_Cyclic_Multi-Variate_Function_for_Self-Supervised_Image_Denoising_by_Disentangling_CVPR_2022_paper.html | CVPR 2022 | null |
Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World | Xiaopei Zhu, Zhanhao Hu, Siyuan Huang, Jianmin Li, Xiaolin Hu | Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern. The core of our method is to design a basic pattern that can be expanded periodically, and make the pattern after random cropping and deformation still have an adversarial effect, then we can process the flat cloth with an adversarial pattern into any 3D clothes. The results showed that the optimized "QR code" pattern lowered the Average Precision (AP) of YOLOv3 by 87.7%, while the random "QR code" pattern and blank pattern lowered the AP of YOLOv3 by 57.9% and 30.1%, respectively, in the digital world. We then manufactured an adversarial shirt with a new material: aerogel. Physical-world experiments showed that the adversarial "QR code" pattern clothing lowered the AP of YOLOv3 by 64.6%, while the random "QR code" pattern clothing and fully heat-insulated clothing lowered the AP of YOLOv3 by 28.3% and 22.8%, respectively. We used the model ensemble technique to improve the attack transferability to unseen models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Infrared_Invisible_Clothing_Hiding_From_Infrared_Detectors_at_Multiple_Angles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Infrared_Invisible_Clothing_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.05909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Infrared_Invisible_Clothing_Hiding_From_Infrared_Detectors_at_Multiple_Angles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Infrared_Invisible_Clothing_Hiding_From_Infrared_Detectors_at_Multiple_Angles_CVPR_2022_paper.html | CVPR 2022 | null |
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation | Zitian Wang, Xuecheng Nie, Xiaochao Qu, Yunpeng Chen, Si Liu | In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem. Different from existing top-down and bottom-up methods, the proposed DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camera space in a one-pass manner. This leads to a simplified pipeline with enhanced efficiency. In addition, DAS learns the true distribution of body joints for the regression of their positions, rather than making a simple Laplacian or Gaussian assumption as previous works. This provides valuable priors for model prediction and thus boosts the regression-based scheme to achieve competitive performance with volumetric-base ones. Moreover, DAS exploits a recursive update strategy for progressively approaching to regression target, alleviating the optimization difficulty and further lifting the regression performance. DAS is implemented with a fully Convolutional Neural Network and end-to-end learnable. Comprehensive experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior efficiency of the proposed DAS model, specifically 1.5x speedup over previous best model, and its stat-of-the-art accuracy for multi-person 3D pose estimation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Distribution-Aware_Single-Stage_Models_for_Multi-Person_3D_Pose_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.07697 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Distribution-Aware_Single-Stage_Models_for_Multi-Person_3D_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Distribution-Aware_Single-Stage_Models_for_Multi-Person_3D_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
FaceFormer: Speech-Driven 3D Facial Animation With Transformers | Yingruo Fan, Zhaojiang Lin, Jun Saito, Wenping Wang, Taku Komura | Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on learning phoneme-level features of short audio windows with limited context, occasionally resulting in inaccurate lip movements. To tackle this limitation, we propose a Transformer-based autoregressive model, FaceFormer, which encodes the long-term audio context and autoregressively predicts a sequence of animated 3D face meshes. To cope with the data scarcity issue, we integrate the self-supervised pre-trained speech representations. Also, we devise two biased attention mechanisms well suited to this specific task, including the biased cross-modal multi-head (MH) attention and the biased causal MH self-attention with a periodic positional encoding strategy. The former effectively aligns the audio-motion modalities, whereas the latter offers abilities to generalize to longer audio sequences. Extensive experiments and a perceptual user study show that our approach outperforms the existing state-of-the-arts. The code and the video are available at: https://evelynfan.github.io/audio2face/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_FaceFormer_Speech-Driven_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.05329 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Patch-Wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks | Chanyong Jung, Gihyun Kwon, Jong Chul Ye | Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency (SRC) regularization along with the decoupled contrastive learning (DCL), which utilize the diverse semantics by focusing on the heterogeneous semantics between the image patches of a single image. To further improve the performance, we present a hard negative mining by exploiting the semantic relation. We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. Experimental results confirmed the state-of-art performance of our method in all the three tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jung_Exploring_Patch-Wise_Semantic_Relation_for_Contrastive_Learning_in_Image-to-Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jung_Exploring_Patch-Wise_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01532 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jung_Exploring_Patch-Wise_Semantic_Relation_for_Contrastive_Learning_in_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jung_Exploring_Patch-Wise_Semantic_Relation_for_Contrastive_Learning_in_Image-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
High-Resolution Face Swapping via Latent Semantics Disentanglement | Yangyang Xu, Bailin Deng, Junle Wang, Yanqing Jing, Jia Pan, Shengfeng He | We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure attributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending to produce a plausible swapping result. We further extend our method to video face swapping by enforcing two spatio-temporal constraints on the latent space and the image space. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_High-Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_High-Resolution_Face_Swapping_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_High-Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_High-Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.html | CVPR 2022 | null |
Searching the Deployable Convolution Neural Networks for GPUs | Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit Florea | Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we build a distributed NAS system to search on a novel search space that consists of prominent factors to impact latency and accuracy. Since we target GPU, we name the NAS optimized models as GPUNet, which establishes a new SOTA Pareto frontier in inference latency and accuracy. Within 1ms, GPUNet is 2x faster than EfficientNet-X and FBNetV3 with even better accuracy. We also validate GPUNet on detection tasks, and GPUNet consistently outperforms EfficientNet-X and FBNetV3 on COCO detection tasks in both latency and accuracy. All of these data validate that our NAS system is effective and generic to handle different design tasks. With this NAS system, we expand GPUNet to cover more latency groups to be directly reusable to DL practitioners in various deployment scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Searching_the_Deployable_Convolution_Neural_Networks_for_GPUs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Searching_the_Deployable_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.00841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Searching_the_Deployable_Convolution_Neural_Networks_for_GPUs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Searching_the_Deployable_Convolution_Neural_Networks_for_GPUs_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning | Jiahao Xia, Weiwei Qu, Wenjian Huang, Jianguo Zhang, Xi Wang, Min Xu | Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Sparse_Local_Patch_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.html | CVPR 2022 | null |
DeepFake Disrupter: The Detector of DeepFake Is My Friend | Xueyu Wang, Jiajun Huang, Siqi Ma, Surya Nepal, Chang Xu | In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would become a real threat to both celebrities and ordinary people. Precautions such as adding perturbations to the source inputs will make DeepFake results look distorted from the perspective of human eyes. However, previous method doesn't explore whether the disrupted images can still spoof DeepFake detectors. This is critical for many applications where DeepFake detectors are used to discriminate between DeepFake data and real data due to the huge cost of examining a large amount of data manually. We argue that the detectors do not share a similar perspective as human eyes, which might still be spoofed by the disrupted data. Besides, the existing disruption methods rely on iteration-based perturbation generation algorithms, which is time-consuming. In this paper, we propose a novel DeepFake disruption algorithm called "DeepFake Disrupter". By training a perturbation generator, we can add the human-imperceptible perturbations to source images that need to be protected without any backpropagation update. The DeepFake results of these protected source inputs would not only look unrealistic by the human eye but also can be distinguished by DeepFake detectors easily. For example, experimental results show that by adding our trained perturbations, fake images generated by StarGAN can result in a 10 20% increase in F1-score evaluated by various DeepFake detectors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_DeepFake_Disrupter_The_Detector_of_DeepFake_Is_My_Friend_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_DeepFake_Disrupter_The_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_DeepFake_Disrupter_The_Detector_of_DeepFake_Is_My_Friend_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_DeepFake_Disrupter_The_Detector_of_DeepFake_Is_My_Friend_CVPR_2022_paper.html | CVPR 2022 | null |
Rotationally Equivariant 3D Object Detection | Hong-Xing Yu, Jiajun Wu, Li Yi | Rotation equivariance has recently become a strongly desired property in the 3D deep learning community. Yet most existing methods focus on equivariance regarding a global input rotation while ignoring the fact that rotation symmetry has its own spatial support. Specifically, we consider the object detection problem in 3D scenes, where an object bounding box should be equivariant regarding the object pose, independent of the scene motion. This suggests a new desired property we call object-level rotation equivariance. To incorporate object-level rotation equivariance into 3D object detectors, we need a mechanism to extract equivariant features with local object-level spatial support while being able to model cross-object context information. To this end, we propose Equivariant Object detection Network (EON) with a rotation equivariance suspension design to achieve object-level equivariance. EON can be applied to modern point cloud object detectors, such as VoteNet and PointRCNN, enabling them to exploit object rotation symmetry in scene-scale inputs. Our experiments on both indoor scene and autonomous driving datasets show that significant improvements are obtained by plugging our EON design into existing state-of-the-art 3D object detectors. Project website: https://kovenyu.com/EON/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Rotationally_Equivariant_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Rotationally_Equivariant_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Rotationally_Equivariant_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Rotationally_Equivariant_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Accelerating DETR Convergence via Semantic-Aligned Matching | Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Kaiwen Cui, Shijian Lu | The recently developed DEtection TRansformer (DETR) establishes a new object detection paradigm by eliminating a series of hand-crafted components. However, DETR suffers from extremely slow convergence, which increases the training cost significantly. We observe that the slow convergence is largely attributed to the complication in matching object queries with target features in different feature embedding spaces. This paper presents SAM-DETR, a Semantic-Aligned-Matching DETR that greatly accelerates DETR's convergence without sacrificing its accuracy. SAM-DETR addresses the convergence issue from two perspectives. First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics. Second, it explicitly searches salient points with the most discriminative features for semantic-aligned matching, which further speeds up the convergence and boosts detection accuracy as well. Being like a plug and play, SAM-DETR complements existing convergence solutions well yet only introduces slight computational overhead. Extensive experiments show that the proposed SAM-DETR achieves superior convergence as well as competitive detection accuracy. The implementation codes are publicly available at https://github.com/ZhangGongjie/SAM-DETR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Accelerating_DETR_Convergence_via_Semantic-Aligned_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Accelerating_DETR_Convergence_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06883 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Accelerating_DETR_Convergence_via_Semantic-Aligned_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Accelerating_DETR_Convergence_via_Semantic-Aligned_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Short Temporal Contrastive Learning of Video Transformers | Jue Wang, Gedas Bertasius, Du Tran, Lorenzo Torresani | Video transformers have recently emerged as a competitive alternative to 3D CNNs for video understanding. However, due to their large number of parameters and reduced inductive biases, these models require supervised pretraining on large-scale image datasets to achieve top performance. In this paper, we empirically demonstrate that self-supervised pretraining of video transformers on video-only datasets can lead to action recognition results that are on par or better than those obtained with supervised pretraining on large-scale image datasets, even massive ones such as ImageNet-21K. Since transformer-based models are effective at capturing dependencies over extended temporal spans, we propose a simple learning procedure that forces the model to match a long-term view to a short-term view of the same video. Our approach, named Long-Short Temporal Contrastive Learning (LSTCL), enables video transformers to learn an effective clip-level representation by predicting temporal context captured from a longer temporal extent. To demonstrate the generality of our findings, we implement and validate our approach under three different self-supervised contrastive learning frameworks (MoCo v3, BYOL, SimSiam) using two distinct video-transformer architectures, including an improved variant of the Swin Transformer augmented with space-time attention. We conduct a thorough ablation study and show that LSTCL achieves competitive performance on multiple video benchmarks and represents a convincing alternative to supervised image-based pretraining. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Long-Short_Temporal_Contrastive_Learning_of_Video_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.09212 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Long-Short_Temporal_Contrastive_Learning_of_Video_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Long-Short_Temporal_Contrastive_Learning_of_Video_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Vision Transformer With Deformable Attention | Zhuofan Xia, Xuran Pan, Shiji Song, Li Erran Li, Gao Huang | Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher representation power over their CNN counterparts. Nevertheless, simply enlarging receptive field also gives rise to several concerns. On the one hand, using dense attention e.g., in ViT, leads to excessive memory and computational cost, and features can be influenced by irrelevant parts which are beyond the region of interests. On the other hand, the sparse attention adopted in PVT or Swin Trans-former is data agnostic and may limit the ability to model long range relations. To mitigate these issues, we propose a novel deformable self-attention module, where the positions of key and value pairs in self-attention are selected in a data-dependent way. This flexible scheme enables the self-attention module to focus on relevant regions and cap-ture more informative features. On this basis, we present Deformable Attention Transformer, a general backbone model with deformable attention for both image classifi-cation and dense prediction tasks. Extensive experiments show that our models achieve consistently improved results on comprehensive benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Vision_Transformer_With_Deformable_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Vision_Transformer_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.00520 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Vision_Transformer_With_Deformable_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Vision_Transformer_With_Deformable_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture | Tanmay Gupta, Amita Kamath, Aniruddha Kembhavi, Derek Hoiem | Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires non-trivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process. In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept transfer, and learning efficiency that may inform future work on general purpose vision. Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_Towards_General_Purpose_Vision_Systems_An_End-to-End_Task-Agnostic_Vision-Language_Architecture_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gupta_Towards_General_Purpose_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_Towards_General_Purpose_Vision_Systems_An_End-to-End_Task-Agnostic_Vision-Language_Architecture_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_Towards_General_Purpose_Vision_Systems_An_End-to-End_Task-Agnostic_Vision-Language_Architecture_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish | Yancong Lin, Ruben Wiersma, Silvia L. Pintea, Klaus Hildebrandt, Elmar Eisemann, Jan C. van Gemert | Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even slightly different domains, and minor problem variants. Here, we address these issues by injecting deep vanishing point detection networks with prior knowledge. This prior knowledge no longer needs to be learned from data, saving valuable annotation efforts and compute, unlocking realistic few-sample scenarios, and reducing the impact of domain changes. Moreover, the interpretability of the priors allows to adapt deep networks to minor problem variations such as switching between Manhattan and non-Manhattan worlds. We seamlessly incorporate two geometric priors: (i) Hough Transform -- mapping image pixels to straight lines, and (ii) Gaussian sphere -- mapping lines to great circles whose intersections denote vanishing points. Experimentally, we ablate our choices and show comparable accuracy to existing models in the large-data setting. We validate our model's improved data efficiency, robustness to domain changes, adaptability to non-Manhattan settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Deep_Vanishing_Point_Detection_Geometric_Priors_Make_Dataset_Variations_Vanish_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Deep_Vanishing_Point_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08586 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Deep_Vanishing_Point_Detection_Geometric_Priors_Make_Dataset_Variations_Vanish_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Deep_Vanishing_Point_Detection_Geometric_Priors_Make_Dataset_Variations_Vanish_CVPR_2022_paper.html | CVPR 2022 | null |
RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes | Tak-Wai Hui | Unsupervised methods have showed promising results on monocular depth estimation. However, the training data must be captured in scenes without moving objects. To push the envelope of accuracy, recent methods tend to increase their model parameters. In this paper, an unsupervised learning framework is proposed to jointly predict monocular depth and complete 3D motion including the motions of moving objects and camera. (1) Recurrent modulation units are used to adaptively and iteratively fuse encoder and decoder features. This improves the single-image depth inference without overspending model parameters. (2) Instead of using a single set of filters for upsampling, multiple sets of filters are devised for the residual upsampling. This facilitates the learning of edge-preserving filters and leads to the improved performance. (3) A warping-based network is used to estimate a motion field of moving objects without using semantic priors. This breaks down the requirement of scene rigidity and allows to use general videos for the unsupervised learning. The motion field is further regularized by an outlier-aware training loss. Despite the depth model just uses a single image in test time and 2.97M parameters, it achieves state-of-the-art results on the KITTI and Cityscapes benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hui_RM-Depth_Unsupervised_Learning_of_Recurrent_Monocular_Depth_in_Dynamic_Scenes_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_RM-Depth_Unsupervised_Learning_of_Recurrent_Monocular_Depth_in_Dynamic_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_RM-Depth_Unsupervised_Learning_of_Recurrent_Monocular_Depth_in_Dynamic_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
LiT: Zero-Shot Transfer With Locked-Image Text Tuning | Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer | This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning "Locked-image Tuning" (LiT), which just teaches a text model to read out good representations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT is widely applicable; it works reliably with multiple pre-training methods (supervised and unsupervised) and across diverse architectures (ResNet, Vision Transformers and MLP-Mixer) using three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 84.5% zero-shot transfer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhai_LiT_Zero-Shot_Transfer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.07991 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html | CVPR 2022 | null |
Cloning Outfits From Real-World Images to 3D Characters for Generalizable Person Re-Identification | Yanan Wang, Xuezhi Liang, Shengcai Liao | Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoon-like and in random dress collocation, which limits their performance. To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. Specifically, based on UV texture mapping, two cloning methods are designed, namely registered clothes mapping and homogeneous cloth expansion. Given clothes keypoints detected on person images and labeled on regular UV maps with clear clothes structures, registered mapping applies perspective homography to warp real-world clothes to the counterparts on the UV map. As for invisible clothes parts and irregular UV maps, homogeneous expansion segments a homogeneous area on clothes as a realistic cloth pattern or cell, and expand the cell to fill the UV map. Furthermore, a similarity-diversity expansion strategy is proposed, by clustering person images, sampling images per cluster, and cloning outfits for 3D character generation. This way, virtual persons can be scaled up densely in visual similarity to challenge model learning, and diversely in population to enrich sample distribution. Finally, by rendering the cloned characters in Unity3D scenes, a more realistic virtual dataset called ClonedPerson is created, with 5,621 identities and 887,766 images. Experimental results show that the model trained on ClonedPerson has a better generalization performance, superior to that trained on other popular real-world and synthetic person re-identification datasets. The ClonedPerson project is available at https://github.com/Yanan-Wang-cs/ClonedPerson. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Cloning_Outfits_From_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02611 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.html | CVPR 2022 | null |
GeoNeRF: Generalizing NeRF With Geometry Priors | Mohammad Mahdi Johari, Yann Lepoittevin, François Fleuret | We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Transformer-based attention mechanism and the cascaded cost volumes, the renderer infers geometry and appearance, and renders detailed images via classical volume rendering techniques. This architecture, in particular, allows sophisticated occlusion reasoning, gathering information from consistent source views. Moreover, our method can easily be fine-tuned on a single scene, and renders competitive results with per-scene optimized neural rendering methods with a fraction of computational cost. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets. Lastly, with a slight modification to the geometry reasoner, we also propose an alternative model that adapts to RGBD images. This model directly exploits the depth information often available thanks to depth sensors. The implementation code is available at https://www.idiap.ch/paper/geonerf. | https://openaccess.thecvf.com/content/CVPR2022/papers/Johari_GeoNeRF_Generalizing_NeRF_With_Geometry_Priors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Johari_GeoNeRF_Generalizing_NeRF_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13539 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Johari_GeoNeRF_Generalizing_NeRF_With_Geometry_Priors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Johari_GeoNeRF_Generalizing_NeRF_With_Geometry_Priors_CVPR_2022_paper.html | CVPR 2022 | null |
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo | Biwen Lei, Xiefan Guo, Hongyu Yang, Miaomiao Cui, Xuansong Xie, Di Huang | Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid network, which aims to achieve fast local retouching on ultra high-resolution photos. The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL). The LRL is designed to implement local retouching on low-resolution images, giving full consideration of the global context and local texture information, and the BPL is then developed to progressively expand the low-resolution results to the higher ones, with the help of the proposed adaptive blend module and refining module. Our method outperforms the existing methods by a large margin on two local photo retouching tasks and exhibits excellent performance in terms of running speed, achieving real-time inference on 4K images with a single NVIDIA Tesla P100 GPU. Moreover, we introduce the first high-definition cloth retouching dataset CRHD-3K to promote the research on local photo retouching. The dataset is available at https://github.com/youngLBW/CRHD-3K. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lei_ABPN_Adaptive_Blend_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.html | CVPR 2022 | null |
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation With Photometrically Challenging Objects | Pengyuan Wang, HyunJun Jung, Yitong Li, Siyuan Shen, Rahul Parthasarathy Srikanth, Lorenzo Garattoni, Sven Meier, Nassir Navab, Benjamin Busam | Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to be supported by well-designed datasets. To provide a benchmark with high-quality ground truth annotations to the community, we introduce a multimodal dataset for category-level object pose estimation with photometrically challenging objects termed PhoCaL. PhoCaL comprises 60 high quality 3D models of household objects over 8 categories including highly reflective, transparent and symmetric objects. We developed a novel robot-supported multi-modal (RGB, depth, polarisation) data acquisition and annotation process. It ensures sub-millimeter accuracy of the pose for opaque textured, shiny and transparent objects, no motion blur and perfect camera synchronisation. To set a benchmark for our dataset, state-of-the-art RGB-D and monocular RGB methods are evaluated on the challenging scenes of PhoCaL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_PhoCaL_A_Multi-Modal_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.08811 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Compression-Based Feature Learning for Video Restoration | Cong Huang, Jiahao Li, Bin Li, Dong Liu, Yan Lu | Most existing deep learning (DL)-based video restoration methods focus on the network structure design to better extract temporal features but ignore how to utilize these extracted temporal features efficiently. The temporal features usually contain various noisy and irrelative information, and they may interfere with the restoration of the current frame. This paper proposes learning noise-robust feature representations to help video restoration. From information theory, we know the noisy data generally has a high degree of uncertainty, thus we design a neural compression module to filter the noise with large uncertainty and refine the features. Our compression module adopts a spatial-channel-wise quantization mechanism to adaptively filter the noise and purify the features with different content characteristics to achieve robustness to noise. The information entropy loss is used to guide the learning of the compression module and helps it preserve the most useful information. Experiments show that our method can significantly boost the performance on video denoising. Under noise level 50, we obtain 0.13 dB improvement over BasicVSR++ with only 0.23x FLOPs. Meanwhile, our method also achieves SOTA results on video deraining and dehazing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Neural_Compression-Based_Feature_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
Expanding Low-Density Latent Regions for Open-Set Object Detection | Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia | Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categories are often misclassified to existing known classes. In this work, we propose to identify unknown objects by separating high/low-density regions in the latent space, based on the consensus that unknown objects are usually distributed in low-density latent regions. As traditional threshold-based methods only maintain limited low-density regions, which cannot cover all unknown objects, we present a novel Open-set Detector (OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner (UPL). CFL performs instance-level contrastive learning to encourage compact features of known classes, leaving more low-density regions for unknown classes; UPL optimizes unknown probability based on the uncertainty of predictions, which further divides more low-density regions around the cluster of known classes. Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability. Extensive experiments demonstrate that our method can significantly improve the OSOD performance, e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD benchmarks. Code is available at: https://github.com/csuhan/opendet2. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Expanding_Low-Density_Latent_Regions_for_Open-Set_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Expanding_Low-Density_Latent_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14911 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Expanding_Low-Density_Latent_Regions_for_Open-Set_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Expanding_Low-Density_Latent_Regions_for_Open-Set_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Drop the GAN: In Defense of Patches Nearest Neighbors As Single Image Generative Models | Niv Granot, Ben Feinstein, Assaf Shocher, Shai Bagon, Michal Irani | Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered "DL-only". However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed -- classical methods can be adapted to tackle these novel "GAN-only" tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image; (ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively); (iii) they are orders of magnitude faster (runtime reduced from hours to seconds). | https://openaccess.thecvf.com/content/CVPR2022/papers/Granot_Drop_the_GAN_In_Defense_of_Patches_Nearest_Neighbors_As_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2103.15545 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Granot_Drop_the_GAN_In_Defense_of_Patches_Nearest_Neighbors_As_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Granot_Drop_the_GAN_In_Defense_of_Patches_Nearest_Neighbors_As_CVPR_2022_paper.html | CVPR 2022 | null |
Uformer: A General U-Shaped Transformer for Image Restoration | Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, Houqiang Li | In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs. First, we introduce a novel locally-enhanced window (LeWin) Transformer block, which performs non-overlapping window-based self-attention instead of global self-attention. It significantly reduces the computational complexity on high resolution feature map while capturing local context. Second, we propose a learnable multi-scale restoration modulator in the form of a multi-scale spatial bias to adjust features in multiple layers of the Uformer decoder. Our modulator demonstrates superior capability for restoring details for various image restoration tasks while introducing marginal extra parameters and computational cost. Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration. To evaluate our approach, extensive experiments are conducted on several image restoration tasks, including image denoising, motion deblurring, defocus deblurring and deraining. Without bells and whistles, our Uformer achieves superior or comparable performance compared with the state-of-the-art algorithms. The code and models are available at https://github.com/ZhendongWang6/Uformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Uformer_A_General_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Dual-Task Correlation for Pose Guided Person Image Generation | Pengze Zhang, Lingxiao Yang, Jian-Huang Lai, Xiaohua Xie | Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Transformer Network (DPTN), which introduces an auxiliary task (i.e., source-tosource task) and exploits the dual-task correlation to promote the performance of PGPIG. The DPTN is of a Siamese structure, containing a source-to-source self-reconstruction branch, and a transformation branch for source-to-target generation. By sharing partial weights between them, the knowledge learned by the source-to-source task can effectively assist the source-to-target learning. Furthermore, we bridge the two branches with a proposed Pose Transformer Module (PTM) to adaptively explore the correlation between features from dual tasks. Such correlation can establish the fine-grained mapping of all the pixels between the sources and the targets, and promote the source texture transmission to enhance the details of the generated target images. Extensive experiments show that our DPTN outperforms state-of-the-arts in terms of both PSNR and LPIPS. In addition, our DPTN only contains 9.79 million parameters, which is significantly smaller than other approaches. Our code is available at: https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exploring_Dual-Task_Correlation_for_Pose_Guided_Person_Image_Generation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_Dual-Task_Correlation_for_Pose_Guided_Person_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_Dual-Task_Correlation_for_Pose_Guided_Person_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data | Junfeng Lyu, Zhibo Wang, Feng Xu | In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical in handling these problems. However, completely removing the eyeglasses is challenging because the lighting effects (e.g., cast shadows) caused by them are often complex. In this paper, we propose a novel framework to remove eyeglasses as well as their cast shadows from face images. The method works in a detect-then-remove manner, in which eyeglasses and cast shadows are both detected and then removed from images. Due to the lack of paired data for supervised training, we present a new synthetic portrait dataset with both intermediate and final supervisions for both the detection and removal tasks. Furthermore, we apply a cross-domain technique to fill the gap between the synthetic and real data. To the best of our knowledge, the proposed technique is the first to remove eyeglasses and their cast shadows simultaneously. The code and synthetic dataset are available at https://github.com/StoryMY/take-off-eyeglasses. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lyu_Portrait_Eyeglasses_and_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.10474 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Rays for Occlusion-Aware Image-Based Rendering | Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, Wenping Wang | We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis task. Recent works construct radiance fields from image features of input views to render novel view images, which enables the generalization to new scenes. However, due to occlusions, a 3D point may be invisible to some input views. On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction. To solve this problem, we predict the visibility of 3D points to input views within our NeuRay representation. This visibility enables the radiance field construction to focus on visible image features, which significantly improves its rendering quality. Meanwhile, a novel consistency loss is proposed to refine the visibility in NeuRay when finetuning on a specific scene. Experiments demonstrate that our approach achieves state-of-the-art performance on the novel view synthesis task when generalizing to unseen scenes and outperforms per-scene optimization methods after finetuning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Neural_Rays_for_Occlusion-Aware_Image-Based_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Neural_Rays_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.13421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Rays_for_Occlusion-Aware_Image-Based_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Rays_for_Occlusion-Aware_Image-Based_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
Modeling 3D Layout for Group Re-Identification | Quan Zhang, Kaiheng Dang, Jian-Huang Lai, Zhanxiang Feng, Xiaohua Xie | Group re-identification (GReID) attempts to correctly associate groups with the same members under different cameras. The main challenge is how to resist the membership and layout variations. Existing works attempt to incorporate layout modeling on the basis of appearance features to achieve robust group representations. However, layout ambiguity is introduced because these methods only consider the 2D layout on the imaging plane. In this paper, we overcome the above limitations by 3D layout modeling. Specifically, we propose a novel 3D transformer (3DT) that reconstructs the relative 3D layout relationship among members, then applies sampling and quantification to preset a series of layout tokens along three dimensions, and selects the corresponding tokens as layout features for each member. Furthermore, we build a synthetic GReID dataset, City1M, including 1.84M images, 45K persons and 11.5K groups with 3D annotations to alleviate data shortages and poor annotations. To the best of our knowledge, 3DT is the first work to address GReID with 3D perspective, and the City1M is the currently largest dataset. Several experiments show the superiority of our 3DT and City1M. Our project has been released on https://github.com/LinlyAC/City1M-dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Modeling_3D_Layout_for_Group_Re-Identification_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Modeling_3D_Layout_for_Group_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Modeling_3D_Layout_for_Group_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity | Weiyao Wang, Matt Feiszli, Heng Wang, Jitendra Malik, Du Tran | Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on explicit class semantics obtained from large labeled datasets, and out-of-domain evaluation performance drops significantly. Here we propose a novel approach for mask proposals, Generic Grouping Networks (GGNs), constructed without semantic supervision. Our approach combines a local measure of pixel affinity with instance-level mask supervision, producing a training regimen designed to make the model as generic as the data diversity allows. We introduce a method for predicting Pairwise Affinities (PA), a learned local relationship between pairs of pixels. PA generalizes very well to unseen categories. From PA we construct a large set of pseudo-ground-truth instance masks; combined with human-annotated instance masks we train GGNs and significantly outperform the SOTA on open-world instance segmentation on various benchmarks including COCO, LVIS, ADE20K, and UVO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Open-World_Instance_Segmentation_Exploiting_Pseudo_Ground_Truth_From_Learned_Pairwise_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Open-World_Instance_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Open-World_Instance_Segmentation_Exploiting_Pseudo_Ground_Truth_From_Learned_Pairwise_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Open-World_Instance_Segmentation_Exploiting_Pseudo_Ground_Truth_From_Learned_Pairwise_CVPR_2022_paper.html | CVPR 2022 | null |
SIOD: Single Instance Annotated per Category per Image for Object Detection | Hanjun Li, Xingjia Pan, Ke Yan, Fan Tang, Wei-Shi Zheng | Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled and unlabeled data. In this study, we propose the Single Instance annotated Object Detection (SIOD), requiring only one instance annotation for each existing category in an image. Degraded from inter-task (WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD provides more reliable and rich prior knowledge for mining the rest of unlabeled instances and trades off the annotation cost and performance. Under the SIOD setting, we propose a simple yet effective framework, termed Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label Generating module (SPLG) and a Pixel-level Group Contrastive Learning module (PGCL). SPLG firstly mines latent instances from feature representation space to alleviate the annotation missing problem. To avoid being misled by inaccurate pseudo labels, we propose PGCL to boost the tolerance to false pseudo labels. Extensive experiments on MS COCO verify the feasibility of the SIOD setting and the superiority of the proposed method, which obtains consistent and significant improvements compared to baseline methods and achieves comparable results with fully supervised object detection (FSOD) methods with only 40% instances annotated. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_SIOD_Single_Instance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15353 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Toward Fast, Flexible, and Robust Low-Light Image Enhancement | Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, Zhongxuan Luo | Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios. In this paper, we develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. To be specific, we establish a cascaded illumination learning process with weight sharing to handle this task. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage, producing the gains that only use the single basic block for inference (yet has not been exploited in previous works), which drastically diminishes computation cost. We then define the unsupervised training loss to elevate the model capability that can adapt general scenes. Further, we make comprehensive explorations to excavate SCI's inherent properties (lacking in existing works) including operation-insensitive adaptability (acquiring stable performance under the settings of different simple operations) and model-irrelevant generality (can be applied to illumination-based existing works to improve performance). Finally, plenty of experiments and ablation studies fully indicate our superiority in both quality and efficiency. Applications on low-light face detection and nighttime semantic segmentation fully reveal the latent practical values for SCI. The source code is available at https://github.com/vis-opt-group/SCI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Toward_Fast_Flexible_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.html | CVPR 2022 | null |
Online Learning of Reusable Abstract Models for Object Goal Navigation | Tommaso Campari, Leonardo Lamanna, Paolo Traverso, Luciano Serafini, Lamberto Ballan | In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state of the environment, as perceived by the agent in a certain position and orientation. The perceptions are high-dimensional sensory data (e.g., RGB-D images), and the abstraction is reached by exploiting image segmentation and the Taskonomy model bank. The learning of the Abstract Model is accomplished by executing actions, observing the reached state, and updating the Abstract Model with the acquired information. The learned models are memorized by the agent, and they are reused whenever it recognizes to be in an environment that corresponds to the stored model. We investigate the effectiveness of the proposed approach for the Object Goal Navigation task, relying on public benchmarks. Our results show that the reuse of learned Abstract Models can boost performance on Object Goal Navigation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Campari_Online_Learning_of_Reusable_Abstract_Models_for_Object_Goal_Navigation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.02583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Campari_Online_Learning_of_Reusable_Abstract_Models_for_Object_Goal_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Campari_Online_Learning_of_Reusable_Abstract_Models_for_Object_Goal_Navigation_CVPR_2022_paper.html | CVPR 2022 | null |
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos | Muheng Li, Lei Chen, Yueqi Duan, Zhilan Hu, Jianjiang Feng, Jie Zhou, Jiwen Lu | Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaningful human activities. Conventional action recognition approaches focus on analyzing single actions. However, they fail to fully reason about the contextual relations between adjacent actions, which provide potential temporal logic for understanding long videos. In this paper, we propose a prompt-based framework, Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so that it simultaneously exploits both out-of-context and contextual information from a series of ordinal actions in instructional videos. More specifically, we reformulate the individual action labels as integrated text prompts for supervision, which bridge the gap between individual action semantics. The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach. The learned vision encoder has a stronger capability for ordinal-action-related downstream tasks, e.g. action segmentation and human activity recognition. We evaluate the performances of our approach on several video datasets: Georgia Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dataset. Br-Prompt achieves state-of-the-art on multiple benchmarks. Code is available at: https://github.com/ttlmh/Bridge-Prompt. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
SimMatch: Semi-Supervised Learning With Similarity Matching | Mingkai Zheng, Shan You, Lang Huang, Fei Wang, Chen Qian, Chang Xu | Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer to fully leverage the ground truth labels on instance-level and bridge the gaps between the semantic and instance similarities. Finally, we proposed the unfolding and aggregation operation which allows these two similarities be isomorphically transformed with each other. In this way, the semantic and instance pseudo-labels can be mutually propagated to generate more high-quality and reliable matching targets. Extensive experimental results demonstrate that SimMatch improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Notably, with 400 epochs of training, SimMatch achieves 67.2%, and 74.4% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet, which significantly outperforms the baseline methods and is better than previous semi-supervised learning frameworks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.06915 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks | Wanyu Lin, Hao Lan, Hao Wang, Baochun Li | This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distinct generative model and design an objective function that encourages the generative model to produce causal, compact, and faithful explanations. This is achieved by isolating the causal factors in the latent space of graphs by maximizing the information flow measurements. We theoretically analyze the cause-effect relationships in the proposed causal graph, identify node attributes as confounders between graphs and GNN predictions, and circumvent such confounder effect by leveraging the backdoor adjustment formula. Our framework is compatible with any GNNs, and it does not require access to the process by which the target GNN produces its predictions. In addition, it does not rely on the linear-independence assumption of the explained features, nor require prior knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on canonical classification problems on graph data. In particular, we analyze the explanatory subgraphs obtained from explanations for molecular graphs (i.e., Mutag) and quantitatively evaluate the explanation performance with frequently occurring subgraph patterns. Empirically, we show that OrphicX can effectively identify the causal semantics for generating causal explanations, significantly outperforming its alternatives. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_OrphicX_A_Causality-Inspired_Latent_Variable_Model_for_Interpreting_Graph_Neural_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_OrphicX_A_Causality-Inspired_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15209 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OrphicX_A_Causality-Inspired_Latent_Variable_Model_for_Interpreting_Graph_Neural_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OrphicX_A_Causality-Inspired_Latent_Variable_Model_for_Interpreting_Graph_Neural_CVPR_2022_paper.html | CVPR 2022 | null |
HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network | JoonKyu Park, Yeonguk Oh, Gyeongsik Moon, Hongsuk Choi, Kyoung Mu Lee | Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D hand mesh estimation network HandOccNet, that can fully exploits the information at occluded regions as a secondary means to enhance image features and make it much richer. To this end, we design two successive Transformer-based modules, called feature injecting transformer (FIT) and self-enhancing transformer (SET). FIT injects hand information into occluded region by considering their correlation. SET refines the output of FIT by using a self-attention mechanism. By injecting the hand information to the occluded region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh benchmarks that contain challenging hand-object occlusions. The codes are available in: https://github.com/namepllet/HandOccNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_HandOccNet_Occlusion-Robust_3D_Hand_Mesh_Estimation_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_HandOccNet_Occlusion-Robust_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14564 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_HandOccNet_Occlusion-Robust_3D_Hand_Mesh_Estimation_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_HandOccNet_Occlusion-Robust_3D_Hand_Mesh_Estimation_Network_CVPR_2022_paper.html | CVPR 2022 | null |
EfficientNeRF Efficient Neural Radiance Fields | Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, Jiaya Jia | Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes. It takes long per-scene training time and per-image testing time. In this paper, we present EfficientNeRF as an efficient NeRF-based method to represent 3D scene and synthesize novel-view images. Although several ways exist to accelerate the training or testing process, it is still difficult to much reduce time for both phases simultaneously. We analyze the density and weight distribution of the sampled points then propose valid and pivotal sampling at the coarse and fine stage, respectively, to significantly improve sampling efficiency. In addition, we design a novel data structure to cache the whole scene during testing to accelerate the testing speed. Overall, our method can reduce over 88% of training time, reach testing speed of around 200 to 500 FPS, while still achieving competitive accuracy. Experiments prove that our method promotes the practicality of NeRF in the real world and enables many applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_EfficientNeRF__Efficient_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_EfficientNeRF__Efficient_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2206.00878 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_EfficientNeRF__Efficient_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_EfficientNeRF__Efficient_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Quantifying Societal Bias Amplification in Image Captioning | Yusuke Hirota, Yuta Nakashima, Noa Garcia | We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each metric, and propose LIC, a metric to study captioning bias amplification. We argue that, for image captioning, it is not enough to focus on the correct prediction of the protected attribute, and the whole context should be taken into account. We conduct extensive evaluation on traditional and state-of-the-art image captioning models, and surprisingly find that, by only focusing on the protected attribute prediction, bias mitigation models are unexpectedly amplifying bias. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hirota_Quantifying_Societal_Bias_Amplification_in_Image_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hirota_Quantifying_Societal_Bias_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15395 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hirota_Quantifying_Societal_Bias_Amplification_in_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hirota_Quantifying_Societal_Bias_Amplification_in_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Modular Action Concept Grounding in Semantic Video Prediction | Wei Yu, Wenxin Chen, Songheng Yin, Steve Easterbrook, Animesh Garg | Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inverse problem of action recognition. The challenge of this new task primarily lies in how to effectively inform the model of semantic action information. Inspired by the idea of Mixture of Experts, we embody each abstract label by a structured combination of various visual concept learners and propose a novel video prediction model, Modular Action Concept Network (MAC). Our method is evaluated on two newly designed synthetic datasets, CLEVR-Building-Blocks and Sapien-Kitchen, and one real-world dataset called Tower-Creation. Extensive experiments demonstrate that MAC can correctly condition on given instructions and generate corresponding future frames without need of bounding boxes. We further show that the trained model can make out-of-distribution generalization, be quickly adapted to new object categories and exploit its learnt features for object detection, showing the progression towards higher-level cognitive abilities. More visualizations can be found at http://www.pair.toronto.edu/mac/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Modular_Action_Concept_Grounding_in_Semantic_Video_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Modular_Action_Concept_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.11201 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Modular_Action_Concept_Grounding_in_Semantic_Video_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Modular_Action_Concept_Grounding_in_Semantic_Video_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
StyleSwin: Transformer-Based GAN for High-Resolution Image Generation | Bowen Zhang, Shuyang Gu, Bo Zhang, Jianmin Bao, Dong Chen, Fang Wen, Yong Wang, Baining Guo | Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end, we believe that the local attention is crucial to strike the balance between computational efficiency and modeling capacity. Hence, the proposed generator adopts Swin transformer in a style-based architecture. To achieve larger receptive field, we propose double attention which simultaneously leverages the context of the local and the shifted windows, leading to improved generation quality. Moreover, we show that offering the knowledge of the absolute position that has lost in window-based transformers greatly benefits the generation quality. The proposed StyleSwin is scalable to high resolutions, with both the coarse geometry and fine structures benefit from the strong expressivity of transformers. However, blocking artifacts occur during high-resolution synthesis because performing the local attention in a block-wise manner may break the spatial coherency. To solve this, we empirically investigate various solutions, among which we find that employing a wavelet discriminator to examine the spectral discrepancy effectively suppresses the artifacts. Extensive experiments show the superiority over prior transformer-based GANs, especially on high resolutions, e.g., 1024x1024. The StyleSwin, without complex training strategies, excelling over StyleGAN on CelebA-HQ 1024, and achieves on-par performance on FFHQ-1024, proving the promise of using transformers for high-resolution image generation. The code and pretrained models are available at https://github.com/microsoft/StyleSwin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_StyleSwin_Transformer-Based_GAN_for_High-Resolution_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_StyleSwin_Transformer-Based_GAN_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.10762 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_StyleSwin_Transformer-Based_GAN_for_High-Resolution_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_StyleSwin_Transformer-Based_GAN_for_High-Resolution_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Reinforced Structured State-Evolution for Vision-Language Navigation | Jinyu Chen, Chen Gao, Erli Meng, Qiong Zhang, Si Liu | Vision-and-language Navigation (VLN) task requires an embodied agent to navigate to a remote location following a natural language instruction. Previous methods usually adopt a sequence model (e.g., Transformer and LSTM) as the navigator. In such a paradigm, the sequence model predicts action at each step through a maintained navigation state, which is generally represented as a one-dimensional vector. However, the crucial navigation clues (i.e., object-level environment layout) for embodied navigation task is discarded since the maintained vector is essentially unstructured. In this paper, we propose a novel Structured state-Evolution (SEvol) model to effectively maintain the environment layout clues for VLN. Specifically, we utilise the graph-based feature to represent the navigation state instead of the vector-based state. Accordingly, we devise a Reinforced Layout clues Miner (RLM) to mine and detect the most crucial layout graph for long-term navigation via a customised reinforcement learning strategy. Moreover, the Structured Evolving Module (SEM) is proposed to maintain the structured graph-based state during navigation, where the state is gradually evolved to learn the object-level spatial-temporal relationship. The experiments on the R2R and R4R datasets show that the proposed SEvol model improves VLN models' performance by large margins, e.g., +3% absolute SPL accuracy for NvEM and +8% for EnvDrop on the R2R test set. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.09280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.html | CVPR 2022 | null |
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{
"title": "Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification",
"authors": "Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, Yi Shan",
"abstract": "Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Dual_Cross-Attention_Learning_CVPR_2022_supplemental.pdf",
"arXiv": "http://arxiv.org/abs/2205.02151",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"tags": "CVPR 2022"
}
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