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title string | authors string | abstract string | pdf string | supp string | arXiv string | bibtex string | url string | detail_url string | tags string | string |
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Memory-Augmented Non-Local Attention for Video Super-Resolution | Jiyang Yu, Jingen Liu, Liefeng Bo, Tao Mei | In this paper, we propose a simple yet effective video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods predominantly leverage temporal neighbor frames to assist the super-resolution of the current frame. Those methods achieve limi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Memory-Augmented_Non-Local_Attention_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2108.11048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis | Yurui Ren, Xiaoqing Fan, Ge Li, Shan Liu, Thomas H. Li | We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference im... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.06160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Texture_Extraction_and_Distribution_for_Controllable_Person_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Classification-Then-Grounding: Reformulating Video Scene Graphs As Temporal Bipartite Graphs | Kaifeng Gao, Long Chen, Yulei Niu, Jian Shao, Jun Xiao | Today's VidSGG models are all proposal-based methods, i.e., they first generate numerous paired subject-object snippets as proposals, and then conduct predicate classification for each proposal. In this paper, we argue that this prevalent proposal-based framework has three inherent drawbacks: 1) The ground-truth predic... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_Classification-Then-Grounding_Reformulating_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_Classification-Then-Grounding_Reformulating_Video_Scene_Graphs_As_Temporal_Bipartite_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
Transformer-Empowered Multi-Scale Contextual Matching and Aggregation for Multi-Contrast MRI Super-Resolution | Guangyuan Li, Jun Lv, Yapeng Tian, Qi Dou, Chengyan Wang, Chenliang Xu, Jing Qin | Magnetic resonance imaging (MRI) can present multi-contrast images of the same anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared with SR reconstruction using a single-contrast, multi-contrast SR reconstruction is promising to yield SR images with higher quality by leveraging dive... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Transformer-Empowered_Multi-Scale_Contextual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13963 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Transformer-Empowered_Multi-Scale_Contextual_Matching_and_Aggregation_for_Multi-Contrast_MRI_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
GazeOnce: Real-Time Multi-Person Gaze Estimation | Mingfang Zhang, Yunfei Liu, Feng Lu | Appearance-based gaze estimation aims to predict the 3D eye gaze direction from a single image. While recent deep learning-based approaches have demonstrated excellent performance, they usually assume one calibrated face in each input image and cannot output multi-person gaze in real time. However, simultaneous gaze es... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_GazeOnce_Real-Time_Multi-Person_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.09480 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_GazeOnce_Real-Time_Multi-Person_Gaze_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
GateHUB: Gated History Unit With Background Suppression for Online Action Detection | Junwen Chen, Gaurav Mittal, Ye Yu, Yu Kong, Mei Chen | Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and has to solely rely on the history, i.e., the frames observed so far, to make predictions. It is therefore important to accentuate parts of t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_GateHUB_Gated_History_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_GateHUB_Gated_History_Unit_With_Background_Suppression_for_Online_Action_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Font Generation by Learning Fine-Grained Local Styles | Licheng Tang, Yiyang Cai, Jiaming Liu, Zhibin Hong, Mingming Gong, Minhu Fan, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG pipeline considers characters in a standard font library as content glyphs and transfers them to a new target font by extracting style info... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Few-Shot_Font_Generation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.09965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Few-Shot_Font_Generation_by_Learning_Fine-Grained_Local_Styles_CVPR_2022_paper.html | CVPR 2022 | null |
Bridging Video-Text Retrieval With Multiple Choice Questions | Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, Ping Luo | Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ge_Bridging_Video-Text_Retrieval_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04850 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ge_Bridging_Video-Text_Retrieval_With_Multiple_Choice_Questions_CVPR_2022_paper.html | CVPR 2022 | null |
Depth-Aware Generative Adversarial Network for Talking Head Video Generation | Fa-Ting Hong, Longhao Zhang, Li Shen, Dan Xu | Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving video. Existing works for this task heavily rely on 2D representations (e.g. appearance and motion) learned from the input images. However, dens... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Depth-Aware_Generative_Adversarial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Depth-Aware_Generative_Adversarial_Network_for_Talking_Head_Video_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Dual-Path Image Inpainting With Auxiliary GAN Inversion | Wentao Wang, Li Niu, Jianfu Zhang, Xue Yang, Liqing Zhang | Deep image inpainting can inpaint a corrupted image using a feed-forward inference, but still fails to handle large missing area or complex semantics. Recently, GAN inversion based inpainting methods propose to leverage semantic information in pretrained generator (e.g., StyleGAN) to solve the above issues. Different f... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Dual-Path_Image_Inpainting_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Dual-Path_Image_Inpainting_With_Auxiliary_GAN_Inversion_CVPR_2022_paper.html | CVPR 2022 | null |
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis | Ming Tao, Hao Tang, Fei Wu, Xiao-Yuan Jing, Bing-Kun Bao, Changsheng Xu | Synthesizing high-quality realistic images from text descriptions is a challenging task. Existing text-to-image Generative Adversarial Networks generally employ a stacked architecture as the backbone yet still remain three flaws. First, the stacked architecture introduces the entanglements between generators of differe... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_DF-GAN_A_Simple_and_Effective_Baseline_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Generative Flows With Invertible Attentions | Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, Luc Van Gool | Flow-based generative models have shown an excellent ability to explicitly learn the probability density function of data via a sequence of invertible transformations. Yet, learning attentions in generative flows remains understudied, while it has made breakthroughs in other domains. To fill the gap, this paper introdu... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sukthanker_Generative_Flows_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.03959 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sukthanker_Generative_Flows_With_Invertible_Attentions_CVPR_2022_paper.html | CVPR 2022 | null |
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers | Yunhui Guo, Xudong Wang, Yubei Chen, Stella X. Yu | Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outperforming Euclidean neural networks (ENNs) on datasets with known semantic hierarchies. However, HNN... | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Clipped_Hyperbolic_Classifiers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.11472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.html | CVPR 2022 | null |
Estimating Fine-Grained Noise Model via Contrastive Learning | Yunhao Zou, Ying Fu | Image denoising has achieved unprecedented progress as great efforts have been made to exploit effective deep denoisers. To improve the denoising performance in real-world, two typical solutions are used in recent trends: devising better noise models for the synthesis of more realistic training data, and estimating noi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.01716 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Estimating_Fine-Grained_Noise_Model_via_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
DiffPoseNet: Direct Differentiable Camera Pose Estimation | Chethan M. Parameshwara, Gokul Hari, Cornelia Fermüller, Nitin J. Sanket, Yiannis Aloimonos | Current deep neural network approaches for camera pose estimation rely on scene structure for 3D motion estimation, but this decreases the robustness and thereby makes cross-dataset generalization difficult. In contrast, classical approaches to structure from motion estimate 3D motion utilizing optical flow and then co... | https://openaccess.thecvf.com/content/CVPR2022/papers/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parameshwara_DiffPoseNet_Direct_Differentiable_Camera_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
The Flag Median and FlagIRLS | Nathan Mankovich, Emily J. King, Chris Peterson, Michael Kirby | Finding prototypes (e.g., mean and median) for a dataset is central to a number of common machine learning algorithms. Subspaces have been shown to provide useful, robust representations for datasets of images, videos and more. Since subspaces correspond to points on a Grassmann manifold, one is led to consider the ide... | https://openaccess.thecvf.com/content/CVPR2022/papers/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mankovich_The_Flag_Median_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.04437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mankovich_The_Flag_Median_and_FlagIRLS_CVPR_2022_paper.html | CVPR 2022 | null |
Implicit Feature Decoupling With Depthwise Quantization | Iordanis Fostiropoulos, Barry Boehm | Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where quantization is applied to a decomposed sub-tensor along the feature axis of weak statistical dependence. The feature decomposition leads to an exponential increase in representation capacity w... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fostiropoulos_Implicit_Feature_Decoupling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fostiropoulos_Implicit_Feature_Decoupling_With_Depthwise_Quantization_CVPR_2022_paper.html | CVPR 2022 | null |
Graph-Context Attention Networks for Size-Varied Deep Graph Matching | Zheheng Jiang, Hossein Rahmani, Plamen Angelov, Sue Black, Bryan M. Williams | Deep learning for graph matching has received growing interest and developed rapidly in the past decade. Although recent deep graph matching methods have shown excellent performance on matching between graphs of equal size in the computer vision area, the size-varied graph matching problem, where the number of keypoint... | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Graph-Context_Attention_Networks_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Graph-Context_Attention_Networks_for_Size-Varied_Deep_Graph_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
FENeRF: Face Editing in Neural Radiance Fields | Jingxiang Sun, Xuan Wang, Yong Zhang, Xiaoyu Li, Qi Zhang, Yebin Liu, Jue Wang | Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we pr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.15490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
CoNeRF: Controllable Neural Radiance Fields | Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi | We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attri... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kania_CoNeRF_Controllable_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Noise2NoiseFlow: Realistic Camera Noise Modeling Without Clean Images | Ali Maleky, Shayan Kousha, Michael S. Brown, Marcus A. Brubaker | Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaussian noise or the heteroscedastic Gaussian noise model (a.k.a., camera noise level function) are not sufficient to learn the complex behavio... | https://openaccess.thecvf.com/content/CVPR2022/papers/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Maleky_Noise2NoiseFlow_Realistic_Camera_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2206.01103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Maleky_Noise2NoiseFlow_Realistic_Camera_Noise_Modeling_Without_Clean_Images_CVPR_2022_paper.html | CVPR 2022 | null |
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes | Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu, James Akl, Fadi Alladkani, Ping Hu, Vitaly Ablavsky, Berk Calli, Sarah Adel Bargal, Kate Saenko | Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.02740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bashkirova_ZeroWaste_Dataset_Towards_Deformable_Object_Segmentation_in_Cluttered_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Remember Intentions: Retrospective-Memory-Based Trajectory Prediction | Chenxin Xu, Weibo Mao, Wenjun Zhang, Siheng Chen | To realize trajectory prediction, most previous methods adopt the parameter-based approach, which encodes all the seen past-future instance pairs into model parameters. However, in this way, the model parameters come from all seen instances, which means a huge amount of irrelevant seen instances might also involve in p... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Remember_Intentions_Retrospective-Memory-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11474 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Remember_Intentions_Retrospective-Memory-Based_Trajectory_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Measuring Compositional Consistency for Video Question Answering | Mona Gandhi, Mustafa Omer Gul, Eva Prakash, Madeleine Grunde-McLaughlin, Ranjay Krishna, Maneesh Agrawala | Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is difficult to discern whether models arrive at answers using compositional reasoni... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gandhi_Measuring_Compositional_Consistency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07190 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Category Contrast for Unsupervised Domain Adaptation in Visual Tasks | Jiaxing Huang, Dayan Guan, Aoran Xiao, Shijian Lu, Ling Shao | Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance disc... | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Category_Contrast_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.02885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Category_Contrast_for_Unsupervised_Domain_Adaptation_in_Visual_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering | Vipul Gupta, Zhuowan Li, Adam Kortylewski, Chenyu Zhang, Yingwei Li, Alan Yuille | While Visual Question Answering (VQA) has progressed rapidly, previous works raise concerns about robustness of current VQA models. In this work, we study the robustness of VQA models from a novel perspective: visual context. We suggest that the models over-rely on the visual context, i.e., irrelevant objects in the im... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02285 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_SwapMix_Diagnosing_and_Regularizing_the_Over-Reliance_on_Visual_Context_in_CVPR_2022_paper.html | CVPR 2022 | null |
UNIST: Unpaired Neural Implicit Shape Translation Network | Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang | We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of the art. Furthermore, our translation network is trained to perform the... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_UNIST_Unpaired_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_UNIST_Unpaired_Neural_Implicit_Shape_Translation_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Local-Adaptive Face Recognition via Graph-Based Meta-Clustering and Regularized Adaptation | Wenbin Zhu, Chien-Yi Wang, Kuan-Lun Tseng, Shang-Hong Lai, Baoyuan Wang | Due to the rising concern of data privacy, it's reasonable to assume the local client data can't be transferred to a centralized server, nor their associated identity label is provided. To support continuous learning and fill the last-mile quality gap, we introduce a new problem setup called Local-Adaptive Face Recogni... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Local-Adaptive_Face_Recognition_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14327 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Local-Adaptive_Face_Recognition_via_Graph-Based_Meta-Clustering_and_Regularized_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
The DEVIL Is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting | Ryan Szeto, Jason J. Corso | Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differe... | https://openaccess.thecvf.com/content/CVPR2022/papers/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Szeto_The_DEVIL_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.05332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Szeto_The_DEVIL_Is_in_the_Details_A_Diagnostic_Evaluation_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Mutual Information-Driven Pan-Sharpening | Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, Feng Zhao | Pan-sharpening aims to integrate the complementary information of texture-rich PAN images and multi-spectral (MS) images to produce the texture-rich MS images. Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two mo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Mutual_Information-Driven_Pan-Sharpening_CVPR_2022_paper.html | CVPR 2022 | null |
Shifting More Attention to Visual Backbone: Query-Modulated Refinement Networks for End-to-End Visual Grounding | Jiabo Ye, Junfeng Tian, Ming Yan, Xiaoshan Yang, Xuwu Wang, Ji Zhang, Liang He, Xin Lin | Visual grounding focuses on establishing fine-grained alignment between vision and natural language, which has essential applications in multimodal reasoning systems. Existing methods use pre-trained query-agnostic visual backbones to extract visual feature maps independently without considering the query information. ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Shifting_More_Attention_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15442 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Shifting_More_Attention_to_Visual_Backbone_Query-Modulated_Refinement_Networks_for_CVPR_2022_paper.html | CVPR 2022 | null |
A Framework for Learning Ante-Hoc Explainable Models via Concepts | Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, Vineeth N Balasubramanian | Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and joi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sarkar_A_Framework_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.11761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.html | CVPR 2022 | null |
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior | Davis Rempe, Jonah Philion, Leonidas J. Guibas, Sanja Fidler, Or Litany | Evaluating and improving planning for autonomous vehicles requires scalable generation of long-tail traffic scenarios. To be useful, these scenarios must be realistic and challenging, but not impossible to drive through safely. In this work, we introduce STRIVE, a method to automatically generate challenging scenarios ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rempe_Generating_Useful_Accident-Prone_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.05077 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rempe_Generating_Useful_Accident-Prone_Driving_Scenarios_via_a_Learned_Traffic_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
FLOAT: Factorized Learning of Object Attributes for Improved Multi-Object Multi-Part Scene Parsing | Rishubh Singh, Pranav Gupta, Pradeep Shenoy, Ravikiran Sarvadevabhatla | Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independe... | https://openaccess.thecvf.com/content/CVPR2022/papers/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16168 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLOAT_Factorized_Learning_of_Object_Attributes_for_Improved_Multi-Object_Multi-Part_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Geometry-Aware 3D Generative Adversarial Networks | Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, Gordon Wetzstein | Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the gen... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_Efficient_Geometry-Aware_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.07945 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Efficient_Geometry-Aware_3D_Generative_Adversarial_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks | Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li | In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilib... | https://openaccess.thecvf.com/content/CVPR2022/papers/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Aung_DO-GAN_A_Double_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aung_DO-GAN_A_Double_Oracle_Framework_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Dancing Under the Stars: Video Denoising in Starlight | Kristina Monakhova, Stephan R. Richter, Laura Waller, Vladlen Koltun | Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we demonstrate photorealistic video under starlight (no moon present, <0.001 lux) for the first time. To enab... | https://openaccess.thecvf.com/content/CVPR2022/papers/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Monakhova_Dancing_Under_the_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.04210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Monakhova_Dancing_Under_the_Stars_Video_Denoising_in_Starlight_CVPR_2022_paper.html | CVPR 2022 | null |
FocusCut: Diving Into a Focus View in Interactive Segmentation | Zheng Lin, Zheng-Peng Duan, Zhao Zhang, Chun-Le Guo, Ming-Ming Cheng | Interactive image segmentation is an essential tool in pixel-level annotation and image editing. To obtain a high-precision binary segmentation mask, users tend to add interaction clicks around the object details, such as edges and holes, for efficient refinement. Current methods regard these repair clicks as the guida... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Medial Spectral Coordinates for 3D Shape Analysis | Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar, Herve Lombaert, Michael Gruninger, Dirk Walther, Kaleem Siddiqi | In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision... | https://openaccess.thecvf.com/content/CVPR2022/papers/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13295 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rezanejad_Medial_Spectral_Coordinates_for_3D_Shape_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Contextualized Spatio-Temporal Contrastive Learning With Self-Supervision | Liangzhe Yuan, Rui Qian, Yin Cui, Boqing Gong, Florian Schroff, Ming-Hsuan Yang, Hartwig Adam, Ting Liu | Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes suboptimal for learning spatio-temporally fine-grained features in videos, where scenes and instan... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_Contextualized_Spatio-Temporal_Contrastive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin | Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, there remain fundamental challenges such as the lack of convergence and the potential for catastrophic for... | https://openaccess.thecvf.com/content/CVPR2022/papers/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qu_Rethinking_Architecture_Design_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.06047 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
APES: Articulated Part Extraction From Sprite Sheets | Zhan Xu, Matthew Fisher, Yang Zhou, Deepali Aneja, Rushikesh Dudhat, Li Yi, Evangelos Kalogerakis | Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprit... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_APES_Articulated_Part_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_APES_Articulated_Part_Extraction_From_Sprite_Sheets_CVPR_2022_paper.html | CVPR 2022 | null |
Dressing in the Wild by Watching Dance Videos | Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, Jianchao Yang | While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person misalignment as well as noticeable degradation in fine texture details. This paper, therefore, attends ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Dressing_in_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15320 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Dressing_in_the_Wild_by_Watching_Dance_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
SPAct: Self-Supervised Privacy Preservation for Action Recognition | Ishan Rajendrakumar Dave, Chen Chen, Mubarak Shah | Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dave_SPAct_Self-Supervised_Privacy_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15205 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Uni6D: A Unified CNN Framework Without Projection Breakdown for 6D Pose Estimation | Xiaoke Jiang, Donghai Li, Hao Chen, Ye Zheng, Rui Zhao, Liwei Wu | As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use different backbones to extract features for RGB and depth images. They use a 2D CNN for RGB images and a per-pixel point cloud network for dep... | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Uni6D_A_Unified_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.html | CVPR 2022 | null |
De-Rendering 3D Objects in the Wild | Felix Wimbauer, Shangzhe Wu, Christian Rupprecht | With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks. Large-scale deployment of XR devices and applications means that we cannot solely rely on s... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wimbauer_De-Rendering_3D_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02279 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wimbauer_De-Rendering_3D_Objects_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
SPAMs: Structured Implicit Parametric Models | Pablo Palafox, Nikolaos Sarafianos, Tony Tung, Angela Dai | Parametric 3D models have formed a fundamental role in modeling deformable objects, such as human bodies, faces, and hands; however, the construction of such parametric models requires significant manual intervention and domain expertise. Recently, neural implicit 3D representations have shown great expressibility in c... | https://openaccess.thecvf.com/content/CVPR2022/papers/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Palafox_SPAMs_Structured_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.08141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Palafox_SPAMs_Structured_Implicit_Parametric_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Global Sensing and Measurements Reuse for Image Compressed Sensing | Zi-En Fan, Feng Lian, Jia-Ni Quan | Recently, deep network-based image compressed sensing methods achieved high reconstruction quality and reduced computational overhead compared with traditional methods. However, existing methods obtain measurements only from partial features in the network and use it only once for image reconstruction. They ignore ther... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.pdf | null | https://arxiv.org/abs/2206.11629 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Global_Sensing_and_Measurements_Reuse_for_Image_Compressed_Sensing_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
SeeThroughNet: Resurrection of Auxiliary Loss by Preserving Class Probability Information | Dasol Han, Jaewook Yoo, Dokwan Oh | Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary loss between the feature maps of intermediate layers and the ground truth in the field of semantic segmentation, the size of each feature map must match the ground... | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_SeeThroughNet_Resurrection_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_SeeThroughNet_Resurrection_of_Auxiliary_Loss_by_Preserving_Class_Probability_Information_CVPR_2022_paper.html | CVPR 2022 | null |
Representing 3D Shapes With Probabilistic Directed Distance Fields | Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan D. Jepson | Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks. Yet, explicit shape representations (e.g., voxels, point clouds, meshes), while relatively easily rendered, often suffer from limited geomet... | https://openaccess.thecvf.com/content/CVPR2022/papers/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Aumentado-Armstrong_Representing_3D_Shapes_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aumentado-Armstrong_Representing_3D_Shapes_With_Probabilistic_Directed_Distance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Learning ABCs: Approximate Bijective Correspondence for Isolating Factors of Variation With Weak Supervision | Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia | Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a no... | https://openaccess.thecvf.com/content/CVPR2022/papers/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Murphy_Learning_ABCs_Approximate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.03240 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Murphy_Learning_ABCs_Approximate_Bijective_Correspondence_for_Isolating_Factors_of_Variation_CVPR_2022_paper.html | CVPR 2022 | null |
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding | Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Tomas F. Yago Vicente, Thomas Dideriksen, Himanshu Arora, Matthieu Guillaumin, Jitendra Malik | We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We deri... | https://openaccess.thecvf.com/content/CVPR2022/papers/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Collins_ABO_Dataset_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.06199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
DETReg: Unsupervised Pretraining With Region Priors for Object Detection | Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson | Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture. Instead, we introduce DETReg, a new self-supervised method that pretrains the entire object detection network, including the object localizati... | https://openaccess.thecvf.com/content/CVPR2022/papers/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bar_DETReg_Unsupervised_Pretraining_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04550 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bar_DETReg_Unsupervised_Pretraining_With_Region_Priors_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Restore 3D Face From In-the-Wild Degraded Images | Zhenyu Zhang, Yanhao Ge, Ying Tai, Xiaoming Huang, Chengjie Wang, Hao Tang, Dongjin Huang, Zhifeng Xie | In-the-wild 3D face modelling is a challenging problem as the predicted facial geometry and texture suffer from a lack of reliable clues or priors, when the input images are degraded. To address such a problem, in this paper we propose a novel Learning to Restore (L2R) 3D face framework for unsupervised high-quality fa... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Learning_To_Restore_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Learning_To_Restore_3D_Face_From_In-the-Wild_Degraded_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack | Ye Liu, Yaya Cheng, Lianli Gao, Xianglong Liu, Qilong Zhang, Jingkuan Song | Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking for defense models' lower bound of robustness given a budget number of iterations and a test dataset. A practical evaluation method should be conv... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Practical_Evaluation_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Practical_Evaluation_of_Adversarial_Robustness_via_Adaptive_Auto_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Convolutions for Spatial Interaction Modeling | Zhaoen Su, Chao Wang, David Bradley, Carlos Vallespi-Gonzalez, Carl Wellington, Nemanja Djuric | In many different fields interactions between objects play a critical role in determining their behavior. Graph neural networks (GNNs) have emerged as a powerful tool for modeling interactions, although often at the cost of adding considerable complexity and latency. In this paper, we consider the problem of spatial in... | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Su_Convolutions_for_Spatial_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2104.07182 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Convolutions_for_Spatial_Interaction_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection | Rui Dai, Srijan Das, Kumara Kahatapitiya, Michael S. Ryoo, François Brémond | Action detection is an essential and challenging task, especially for densely labelled datasets of untrimmed videos. The temporal relation is complex in those datasets, including challenges like composite action, and co-occurring action. For detecting actions in those complex videos, efficiently capturing both short-te... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dai_MS-TCT_Multi-Scale_Temporal_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_MS-TCT_Multi-Scale_Temporal_ConvTransformer_for_Action_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Salvage of Supervision in Weakly Supervised Object Detection | Lin Sui, Chen-Lin Zhang, Jianxin Wu | Weakly supervised object detection (WSOD) has recently attracted much attention. However, the lack of bounding-box supervision makes its accuracy much lower than fully supervised object detection (FSOD), and currently modern FSOD techniques cannot be applied to WSOD. To bridge the performance and technical gaps between... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sui_Salvage_of_Supervision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sui_Salvage_of_Supervision_in_Weakly_Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-View Transformers for Real-Time Map-View Semantic Segmentation | Brady Zhou, Philipp Krähenbühl | We present cross-view transformers, an efficient attention-based model for map-view semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping from individual camera views into a canonical map-view representation using a camera-aware cross-view attention mechanism. Each camera uses positi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Cross-View_Transformers_for_Real-Time_Map-View_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning | Hongzu Su, Jingjing Li, Zhi Chen, Lei Zhu, Ke Lu | Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Recognizing unseen classes as seen ones or vice versa often leads to poor performance in GZSL. Therefore, distinguishing seen and unseen domains is naturally an effective yet challenging solution for GZS... | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Su_Distinguishing_Unseen_From_Seen_for_Generalized_Zero-Shot_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Online Continual Learning on a Contaminated Data Stream With Blurry Task Boundaries | Jihwan Bang, Hyunseo Koh, Seulki Park, Hwanjun Song, Jung-Woo Ha, Jonghyun Choi | Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. Large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored. We consider a mo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bang_Online_Continual_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15355 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bang_Online_Continual_Learning_on_a_Contaminated_Data_Stream_With_Blurry_CVPR_2022_paper.html | CVPR 2022 | null |
Controllable Dynamic Multi-Task Architectures | Dripta S. Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K. Roy-Chowdhury, Manmohan Chandraker | Multi-task learning commonly encounters competition for resources among tasks, specifically when model capacity is limited. This challenge motivates models which allow control over the relative importance of tasks and total compute cost during inference time. In this work, we propose such a controllable multi-task netw... | https://openaccess.thecvf.com/content/CVPR2022/papers/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Raychaudhuri_Controllable_Dynamic_Multi-Task_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14949 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Raychaudhuri_Controllable_Dynamic_Multi-Task_Architectures_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Imagine: Diversify Memory for Incremental Learning Using Unlabeled Data | Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng | Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications. Although maintaining a handful of samples (called "exemplars") of each task could alleviate forgetting to some extent, existing methods are still limited by the small number of exemplars si... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Learning_To_Imagine_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Imagine_Diversify_Memory_for_Incremental_Learning_Using_Unlabeled_CVPR_2022_paper.html | CVPR 2022 | null |
SmartAdapt: Multi-Branch Object Detection Framework for Videos on Mobiles | Ran Xu, Fangzhou Mu, Jayoung Lee, Preeti Mukherjee, Somali Chaterji, Saurabh Bagchi, Yin Li | Several recent works seek to create lightweight deep networks for video object detection on mobiles. We observe that many existing detectors, previously deemed computationally costly for mobiles, intrinsically support adaptive inference, and offer a multi-branch object detection framework (MBODF). Here, an MBODF is ref... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_SmartAdapt_Multi-Branch_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_SmartAdapt_Multi-Branch_Object_Detection_Framework_for_Videos_on_Mobiles_CVPR_2022_paper.html | CVPR 2022 | null |
VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks | Yi-Lin Sung, Jaemin Cho, Mohit Bansal | Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this pa... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sung_VL-Adapter_Parameter-Efficient_Transfer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sung_VL-Adapter_Parameter-Efficient_Transfer_Learning_for_Vision-and-Language_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Hybrid Models for Out-of-Distribution Detection | Senqi Cao, Zhongfei Zhang | We propose a principled and practical method for out-of-distribution (OoD) detection with deep hybrid models (DHMs), which model the joint density p(x,y) of features and labels with a single forward pass. By factorizing the joint density p(x,y) into three sources of uncertainty, we show that our approach has the abilit... | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_Deep_Hybrid_Models_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Deep_Hybrid_Models_for_Out-of-Distribution_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Accelerating Video Object Segmentation With Compressed Video | Kai Xu, Angela Yao | We propose an efficient plug-and-play acceleration framework for semi-supervised video object segmentation by exploiting the temporal redundancies in videos presented by the compressed bitstream. Specifically, we propose a motion vector-based warping method for propagating segmentation masks from keyframes to other fra... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Accelerating_Video_Object_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.12192 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Accelerating_Video_Object_Segmentation_With_Compressed_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation | Fan Wang, Zhongyi Han, Yongshun Gong, Yilong Yin | Source-free domain adaptation (SFDA) newly emerges to transfer the relevant knowledge of a well-trained source model to an unlabeled target domain, which is critical in various privacy-preserving scenarios. Most existing methods focus on learning the domain-invariant representations depending solely on the target data,... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Exploring_Domain-Invariant_Parameters_for_Source_Free_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
FastDOG: Fast Discrete Optimization on GPU | Ahmed Abbas, Paul Swoboda | We present a massively parallel Lagrange decomposition method for solving 0--1 integer linear programs occurring in structured prediction. We propose a new iterative update scheme for solving the Lagrangean dual and a perturbation technique for decoding primal solutions. For representing subproblems we follow Lange et ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Abbas_FastDOG_Fast_Discrete_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_FastDOG_Fast_Discrete_Optimization_on_GPU_CVPR_2022_paper.html | CVPR 2022 | null |
Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction | Sara Elkerdawy, Mostafa Elhoushi, Hong Zhang, Nilanjan Ray | Dynamic model pruning is a recent direction that allows for the inference of a different sub-network for each input sample during deployment. However, current dynamic methods rely on learning a continuous channel gating through regularization by inducing sparsity loss. This formulation introduces complexity in balancin... | https://openaccess.thecvf.com/content/CVPR2022/papers/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Elkerdawy_Fire_Together_Wire_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.08232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection | Yifan Wang, Wenbo Zhang, Lijun Wang, Ting Liu, Huchuan Lu | Deep learning-based image salient object detection (SOD) heavily relies on large-scale training data with pixel-wise labeling. High-quality labels involve intensive labor and are expensive to acquire. In this paper, we propose a novel multi-source uncertainty mining method to facilitate unsupervised deep learning from ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multi-Source_Uncertainty_Mining_for_Deep_Unsupervised_Saliency_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Equivariant Learning for Oriented Keypoint Detection | Jongmin Lee, Byungjin Kim, Minsu Cho | Detecting robust keypoints from an image is an integral part of many computer vision problems, and the characteristic orientation and scale of keypoints play an important role for keypoint description and matching. Existing learning-based methods for keypoint detection rely on standard translation-equivariant CNNs but ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Self-Supervised_Equivariant_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Self-Supervised_Equivariant_Learning_for_Oriented_Keypoint_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation | Linfeng Zhang, Xin Chen, Xiaobing Tu, Pengfei Wan, Ning Xu, Kaisheng Ma | Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-the-art GANs usually suffer from low efficiency and bulky memory usage. To tackle this challenge, firstly, this paper investigates GANs perfor... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.06321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Wavelet_Knowledge_Distillation_Towards_Efficient_Image-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Focal and Global Knowledge Distillation for Detectors | Zhendong Yang, Zhe Li, Xiaohu Jiang, Yuan Gong, Zehuan Yuan, Danpei Zhao, Chun Yuan | Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it. In this paper, we point out that in object detection, the features of the teacher and student vary greatly in different areas, esp... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.11837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Focal_and_Global_Knowledge_Distillation_for_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Prompt for Continual Learning | Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister | The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and address forgetting, whil... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Learning_To_Prompt_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.08654 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Prompt_for_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Human Mesh Recovery From Multiple Shots | Georgios Pavlakos, Jitendra Malik, Angjoo Kanazawa | Videos from edited media like movies are a useful, yet under-explored source of information, with rich variety of appearance and interactions between humans depicted over a large temporal context. However, the richness of data comes at the expense of fundamental challenges such as abrupt shot changes and close up shots... | https://openaccess.thecvf.com/content/CVPR2022/papers/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pavlakos_Human_Mesh_Recovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2012.09843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pavlakos_Human_Mesh_Recovery_From_Multiple_Shots_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Adversarial Transferability via Neuron Attribution-Based Attacks | Jianping Zhang, Weibin Wu, Jen-tse Huang, Yizhan Huang, Wenxuan Wang, Yuxin Su, Michael R. Lyu | Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To efficiently tackle the black-box setting where the target model's particulars are unknown, fe... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Improving_Adversarial_Transferability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00008 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Improving_Adversarial_Transferability_via_Neuron_Attribution-Based_Attacks_CVPR_2022_paper.html | CVPR 2022 | null |
Better Trigger Inversion Optimization in Backdoor Scanning | Guanhong Tao, Guangyu Shen, Yingqi Liu, Shengwei An, Qiuling Xu, Shiqing Ma, Pan Li, Xiangyu Zhang | Backdoor attacks aim to cause misclassification of a subject model by stamping a trigger to inputs. Backdoors could be injected through malicious training and naturally exist. Deriving backdoor trigger for a subject model is critical to both attack and defense. A popular trigger inversion method is by optimization. Exi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tao_Better_Trigger_Inversion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Better_Trigger_Inversion_Optimization_in_Backdoor_Scanning_CVPR_2022_paper.html | CVPR 2022 | null |
GANSeg: Learning To Segment by Unsupervised Hierarchical Image Generation | Xingzhe He, Bastian Wandt, Helge Rhodin | Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of video... | https://openaccess.thecvf.com/content/CVPR2022/papers/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_GANSeg_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01036 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Dense Learning Based Semi-Supervised Object Detection | Binghui Chen, Pengyu Li, Xiang Chen, Biao Wang, Lei Zhang, Xian-Sheng Hua | The ultimate goal of semi-supervised object detection (SSOD) is to facilitate the utilization and deployment of detectors in actual applications with the help of a large amount of unlabeled data. Although a few works have proposed various self-training-based methods or consistency-regularization-based methods, they all... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.07300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction | Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan | This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is reckoned with respect to the physical interactions between the object and the user. Given a malfunctional ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Fixing_Malfunctional_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Fixing_Malfunctional_Objects_With_Learned_Physical_Simulation_and_Functional_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Convolution of Convolution: Let Kernels Spatially Collaborate | Rongzhen Zhao, Jian Li, Zhenzhi Wu | In the biological visual pathway, especially the retina, neurons are tiled along spatial dimensions with the electrical coupling as their local association, while in a convolution layer, kernels are placed along the channel dimension singly. We propose Convolution of Convolution, associating kernels in a layer and lett... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Convolution_of_Convolution_Let_Kernels_Spatially_Collaborate_CVPR_2022_paper.html | CVPR 2022 | null |
Make It Move: Controllable Image-to-Video Generation With Text Descriptions | Yaosi Hu, Chong Luo, Zhenzhong Chen | Generating controllable videos conforming to user intentions is an appealing yet challenging topic in computer vision. To enable maneuverable control in line with user intentions, a novel video generation task, named Text-Image-to-Video generation (TI2V), is proposed. With both controllable appearance and motion, TI2V ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Make_It_Move_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02815 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Make_It_Move_Controllable_Image-to-Video_Generation_With_Text_Descriptions_CVPR_2022_paper.html | CVPR 2022 | null |
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection | Tong Wang, Yousong Zhu, Yingying Chen, Chaoyang Zhao, Bin Yu, Jinqiao Wang, Ming Tang | Long-tail object detection suffers from poor performance on tail categories. We reveal that the real culprit lies in the extremely imbalanced distribution of the classifier's weight norm. For conventional softmax cross-entropy loss, such imbalanced weight norm distribution yields ill conditioned decision boundary for c... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_C2AM_Loss_Chasing_a_Better_Decision_Boundary_for_Long-Tail_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Points: Point Cloud Representation With Neural Fields for Arbitrary Upsampling | Wanquan Feng, Jin Li, Hongrui Cai, Xiaonan Luo, Juyong Zhang | In this paper, we propose Neural Points, a novel point cloud representation and apply it to the arbitrary-factored upsampling task. Different from traditional point cloud representation where each point only represents a position or a local plane in the 3D space, each point in Neural Points represents a local continuou... | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.04148 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Neural_Points_Point_Cloud_Representation_With_Neural_Fields_for_Arbitrary_CVPR_2022_paper.html | CVPR 2022 | null |
Distribution Consistent Neural Architecture Search | Junyi Pan, Chong Sun, Yizhou Zhou, Ying Zhang, Chen Li | Recent progress on neural architecture search (NAS) has demonstrated exciting results on automating deep network architecture designs. In order to overcome the unaffordable complexity of training each candidate architecture from scratch, the state-of-the-art one-shot NAS approaches adopt a weight-sharing strategy to im... | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Distribution_Consistent_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Video-Text Representation Learning via Differentiable Weak Temporal Alignment | Dohwan Ko, Joonmyung Choi, Juyeon Ko, Shinyeong Noh, Kyoung-Woon On, Eun-Sol Kim, Hyunwoo J. Kim | Learning generic joint representations for video and text by a supervised method requires a prohibitively substantial amount of manually annotated video datasets. As a practical alternative, a large-scale but uncurated and narrated video dataset, HowTo100M, has recently been introduced. But it is still challenging to l... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ko_Video-Text_Representation_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16784 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Video-Text_Representation_Learning_via_Differentiable_Weak_Temporal_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Bi-Directional Object-Context Prioritization Learning for Saliency Ranking | Xin Tian, Ke Xu, Xin Yang, Lin Du, Baocai Yin, Rynson W.H. Lau | The saliency ranking task is recently proposed to study the visual behavior that humans would typically shift their attention over different objects of a scene based on their degrees of saliency. Existing approaches focus on learning either object-object or object-scene relations. Such a strategy follows the idea of ob... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tian_Bi-Directional_Object-Context_Prioritization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tian_Bi-Directional_Object-Context_Prioritization_Learning_for_Saliency_Ranking_CVPR_2022_paper.html | CVPR 2022 | null |
FreeSOLO: Learning To Segment Objects Without Annotations | Xinlong Wang, Zhiding Yu, Shalini De Mello, Jan Kautz, Anima Anandkumar, Chunhua Shen, Jose M. Alvarez | Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and segmentation masks for learning. In this work, we propose a fully unsupervised learning method that learns class-agnostic instance segmentatio... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_FreeSOLO_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.12181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_FreeSOLO_Learning_To_Segment_Objects_Without_Annotations_CVPR_2022_paper.html | CVPR 2022 | null |
What Do Navigation Agents Learn About Their Environment? | Kshitij Dwivedi, Gemma Roig, Aniruddha Kembhavi, Roozbeh Mottaghi | Today's state of the art visual navigation agents typically consist of large deep learning architectures trained end to end. Such models offer little to no interpretability about the skills learned by the agent or the actions taken by it in response to its environment. While past works have explored interpreting deep l... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dwivedi_What_Do_Navigation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dwivedi_What_Do_Navigation_Agents_Learn_About_Their_Environment_CVPR_2022_paper.html | CVPR 2022 | null |
Progressive Minimal Path Method With Embedded CNN | Wei Liao | We propose Path-CNN, a method for the segmentation of centerlines of tubular structures by embedding convolutional neural networks (CNNs) into the progressive minimal path method. Minimal path methods are widely used for topology-aware centerline segmentation, but usually these methods rely on weak, hand-tuned image fe... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.00944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Progressive_Minimal_Path_Method_With_Embedded_CNN_CVPR_2022_paper.html | CVPR 2022 | null |
FIFO: Learning Fog-Invariant Features for Foggy Scene Segmentation | Sohyun Lee, Taeyoung Son, Suha Kwak | Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images wit... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_FIFO_Learning_Fog-Invariant_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01587 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
3D Human Tongue Reconstruction From Single "In-the-Wild" Images | Stylianos Ploumpis, Stylianos Moschoglou, Vasileios Triantafyllou, Stefanos Zafeiriou | 3D face reconstruction from a single image is a task that has garnered increased interest in the Computer Vision community, especially due to its broad use in a number of applications such as realistic 3D avatar creation, pose invariant face recognition and face hallucination. Since the introduction of the 3D Morphable... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ploumpis_3D_Human_Tongue_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.12302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ploumpis_3D_Human_Tongue_Reconstruction_From_Single_In-the-Wild_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Enhancing Adversarial Robustness for Deep Metric Learning | Mo Zhou, Vishal M. Patel | Owing to security implications of adversarial vulnerability, adversarial robustness of deep metric learning models has to be improved. In order to avoid model collapse due to excessively hard examples, the existing defenses dismiss the min-max adversarial training, but instead learn from a weak adversary inefficiently.... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Enhancing_Adversarial_Robustness_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Enhancing_Adversarial_Robustness_for_Deep_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation | Jiaqi Gu, Hyoukjun Kwon, Dilin Wang, Wei Ye, Meng Li, Yu-Hsin Chen, Liangzhen Lai, Vikas Chandra, David Z. Pan | Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly designed for image classification that generate single-scale low-resolution representations, which makes dense prediction tasks such as semanti... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_Multi-Scale_High-Resolution_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.01236 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Multi-Scale_High-Resolution_Vision_Transformer_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Lite-MDETR: A Lightweight Multi-Modal Detector | Qian Lou, Yen-Chang Hsu, Burak Uzkent, Ting Hua, Yilin Shen, Hongxia Jin | Recent multi-modal detectors based on transformers and modality encoders have successfully achieved impressive results on end-to-end visual object detection conditioned on a raw text query. However, they require a large model size and an enormous amount of computations to achieve high performance, which makes it diffic... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lou_Lite-MDETR_A_Lightweight_Multi-Modal_Detector_CVPR_2022_paper.html | CVPR 2022 | null |
CoordGAN: Self-Supervised Dense Correspondences Emerge From GANs | Jiteng Mu, Shalini De Mello, Zhiding Yu, Nuno Vasconcelos, Xiaolong Wang, Jan Kautz, Sifei Liu | Recent advances show that Generative Adversarial Networks (GANs) can synthesize images with smooth variations along semantically meaningful latent directions, such as pose, expression, layout, etc. While this indicates that GANs implicitly learn pixel-level correspondences across images, few studies explored how to ext... | https://openaccess.thecvf.com/content/CVPR2022/papers/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mu_CoordGAN_Self-Supervised_Dense_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16521 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_CoordGAN_Self-Supervised_Dense_Correspondences_Emerge_From_GANs_CVPR_2022_paper.html | CVPR 2022 | null |
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation | Yutong Chen, Fangyun Wei, Xiao Sun, Zhirong Wu, Stephen Lin | This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pairs of sign videos, gloss annotations and texts, which are an order of magnitude smaller than typical parallel data for training spoken lang... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_A_Simple_Multi-Modality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_A_Simple_Multi-Modality_Transfer_Learning_Baseline_for_Sign_Language_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Visual Representation Learning by Online Constrained K-Means | Qi Qian, Yuanhong Xu, Juhua Hu, Hao Li, Rong Jin | Cluster discrimination is an effective pretext task for unsupervised representation learning, which often consists of two phases: clustering and discrimination. Clustering is to assign each instance a pseudo label that will be used to learn representations in discrimination. The main challenge resides in clustering sin... | https://openaccess.thecvf.com/content/CVPR2022/papers/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qian_Unsupervised_Visual_Representation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Unsupervised_Visual_Representation_Learning_by_Online_Constrained_K-Means_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Point Light Fields | Julian Ost, Issam Laradji, Alejandro Newell, Yuval Bahat, Felix Heide | We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud. Combining differentiable volume rendering with learned implicit density representations has made it possible to synthesize photo-realistic images for novel views of small scenes. As neural volumetr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ost_Neural_Point_Light_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.01473 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ost_Neural_Point_Light_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
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