Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
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Invertible Denoising Network: A Light Solution for Real Noise Removal | Yang Liu, Zhenyue Qin, Saeed Anwar, Pan Ji, Dongwoo Kim, Sabrina Caldwell, Tom Gedeon | Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying invertible models to remove noise is challenging because the input is noisy, and the reversed output is clean, following two different distribution... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10546 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Invertible_Denoising_Network_A_Light_Solution_for_Real_Noise_Removal_CVPR_2021_paper.html | CVPR 2021 | null | null |
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction | Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn | A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. However, while existing video prediction models have produced promising results on small datasets, they suffer from severe underfitting when trained on la... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Greedy_Hierarchical_Variational_Autoencoders_for_Large-Scale_Video_Prediction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Greedy_Hierarchical_Variational_CVPR_2021_supplemental.pdf | null |
Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks | Roi Pony, Itay Naeh, Shie Mannor | Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2002.05123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pony_Over-the-Air_Adversarial_Flickering_Attacks_Against_Video_Recognition_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pony_Over-the-Air_Adversarial_Flickering_CVPR_2021_supplemental.pdf | null |
Encoder Fusion Network With Co-Attention Embedding for Referring Image Segmentation | Guang Feng, Zhiwei Hu, Lihe Zhang, Huchuan Lu | Recently, referring image segmentation has aroused widespread interest. Previous methods perform the multi-modal fusion between language and vision at the decoding side of the network. And, linguistic feature interacts with visual feature of each scale separately, which ignores the continuous guidance of language to mu... | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.01839 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Encoder_Fusion_Network_With_Co-Attention_Embedding_for_Referring_Image_Segmentation_CVPR_2021_paper.html | CVPR 2021 | null | null |
Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo | Seung-Hwan Baek, Felix Heide | Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of object text... | https://openaccess.thecvf.com/content/CVPR2021/papers/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Baek_Polka_Lines_Learning_Structured_Illumination_and_Reconstruction_for_Active_Stereo_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Baek_Polka_Lines_Learning_CVPR_2021_supplemental.zip | null |
Image Inpainting With External-Internal Learning and Monochromic Bottleneck | Tengfei Wang, Hao Ouyang, Qifeng Chen | Although recent inpainting approaches have demonstrated significant improvement with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottle... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Image_Inpainting_With_External-Internal_Learning_and_Monochromic_Bottleneck_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Image_Inpainting_With_CVPR_2021_supplemental.zip | null |
Patch2Pix: Epipolar-Guided Pixel-Level Correspondences | Qunjie Zhou, Torsten Sattler, Laura Leal-Taixe | The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspondence networks propose to perform those steps inside a single network but suffer from low matching resol... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_Correspondences_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Patch2Pix_Epipolar-Guided_Pixel-Level_CVPR_2021_supplemental.pdf | null |
Diverse Part Discovery: Occluded Person Re-Identification With Part-Aware Transformer | Yulin Li, Jianfeng He, Tianzhu Zhang, Xiang Liu, Yongdong Zhang, Feng Wu | Occluded person re-identification (Re-ID) is a challenging task as persons are frequently occluded by various obstacles or other persons, especially in the crowd scenario. To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.04095 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Diverse_Part_Discovery_Occluded_Person_Re-Identification_With_Part-Aware_Transformer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Diverse_Part_Discovery_CVPR_2021_supplemental.pdf | null |
Counterfactual Zero-Shot and Open-Set Visual Recognition | Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang | We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only training on the seen-classes. Our idea stems from the observation that the generated samples for unseen-classes are often out of the true dis... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.00887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yue_Counterfactual_Zero-Shot_and_Open-Set_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yue_Counterfactual_Zero-Shot_and_CVPR_2021_supplemental.pdf | null |
Person30K: A Dual-Meta Generalization Network for Person Re-Identification | Yan Bai, Jile Jiao, Wang Ce, Jun Liu, Yihang Lou, Xuetao Feng, Ling-Yu Duan | Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limitations in multiple ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition | Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford, Tobias Fischer | Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. This paper introduces Patch-NetVLAD, which provides a novel formulation for combining the advantages of both local and global descrip... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_of_Locally-Global_Descriptors_for_Place_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hausler_Patch-NetVLAD_Multi-Scale_Fusion_CVPR_2021_supplemental.pdf | null |
Visually Informed Binaural Audio Generation without Binaural Audios | Xudong Xu, Hang Zhou, Ziwei Liu, Bo Dai, Xiaogang Wang, Dahua Lin | Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating stereophonic audios guided by visual cues and multi-channel audio collections in a fully-supervised manner. However, due to the requirement of professional recording devices,... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06162 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Visually_Informed_Binaural_CVPR_2021_supplemental.pdf | null |
Dual Attention Guided Gaze Target Detection in the Wild | Yi Fang, Jiapeng Tang, Wang Shen, Wei Shen, Xiao Gu, Li Song, Guangtao Zhai | Gaze target detection aims to infer where each person in a scene is looking. Existing works focus on 2D gaze and 2D saliency, but fail to exploit 3D contexts. In this work, we propose a three-stage method to simulate the human gaze inference behavior in 3D space. In the first stage, we introduce a coarse-to-fine strate... | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Dual_Attention_Guided_Gaze_Target_Detection_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Privacy Preserving Localization and Mapping From Uncalibrated Cameras | Marcel Geppert, Viktor Larsson, Pablo Speciale, Johannes L. Schonberger, Marc Pollefeys | Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Geppert_Privacy_Preserving_Localization_and_Mapping_From_Uncalibrated_Cameras_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Geppert_Privacy_Preserving_Localization_CVPR_2021_supplemental.pdf | null |
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling | Wei Ji, Shuang Yu, Junde Wu, Kai Ma, Cheng Bian, Qi Bi, Jingjing Li, Hanruo Liu, Li Cheng, Yefeng Zheng | In medical image analysis, it is typical to collect multiple annotations, each from a different clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated. Meanwhile, from the computer vision practitioner viewpoint, it has been a common practice to adopt the ground-truth obtained vi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Learning_Calibrated_Medical_Image_Segmentation_via_Multi-Rater_Agreement_Modeling_CVPR_2021_paper.html | CVPR 2021 | null | null |
Points As Queries: Weakly Semi-Supervised Object Detection by Points | Liangyu Chen, Tong Yang, Xiangyu Zhang, Wei Zhang, Jian Sun | We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. It achieves a balance between tremendous annotation burden and detection performance. Based on this setting, we analyz... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07434 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Points_As_Queries_Weakly_Semi-Supervised_Object_Detection_by_Points_CVPR_2021_paper.html | CVPR 2021 | null | null |
Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network | Ruicheng Feng, Chongyi Li, Huaijin Chen, Shuai Li, Chen Change Loy, Jinwei Gu | Recent development of Under-Display Camera (UDC) systems provides a true bezel-less and notch-free viewing experience on smartphones (and TV, laptops, tablets), while allowing images to be captured from the selfie camera embedded underneath. In a typical UDC system, the microstructure of the semi-transparent organic li... | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_Removing_Diffraction_Image_Artifacts_in_Under-Display_Camera_via_Dynamic_Skip_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feng_Removing_Diffraction_Image_CVPR_2021_supplemental.pdf | null |
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression | Shifeng Zhang, Chen Zhang, Ning Kang, Zhenguo Li | It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their success on lossless compression, where flow based models are desirable in allowing exact data likelihood optimisation with bijective mappin... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_iVPF_Numerical_Invertible_Volume_Preserving_Flow_for_Efficient_Lossless_Compression_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_iVPF_Numerical_Invertible_CVPR_2021_supplemental.pdf | null |
Pose Recognition With Cascade Transformers | Ke Li, Shijie Wang, Xiang Zhang, Yifan Xu, Weijian Xu, Zhuowen Tu | In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Pose_Recognition_With_Cascade_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection | Zhenyu Wang, Yali Li, Ye Guo, Lu Fang, Shengjin Wang | In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Previous semi-supervised methods based on pseudo labels are severely degenerated by noise and prone to overfit to noisy labels, thus are defici... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Data-Uncertainty_Guided_Multi-Phase_Learning_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Data-Uncertainty_Guided_Multi-Phase_CVPR_2021_supplemental.pdf | null |
Prototype-Guided Saliency Feature Learning for Person Search | Hanjae Kim, Sunghun Joung, Ig-Jae Kim, Kwanghoon Sohn | Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system. Though promising results have been achieved, the misalignment problem, which commonly occurs in person search, limits the discriminative feature representation for re-ID. To overcome this limitation, we... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Prototype-Guided_Saliency_Feature_Learning_for_Person_Search_CVPR_2021_paper.html | CVPR 2021 | null | null |
Contrastive Learning for Compact Single Image Dehazing | Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, Lizhuang Ma | Single image dehazing is a challenging ill-posed problem due to the severe information degeneration. However, existing deep learning based dehazing methods only adopt clear images as positive samples to guide the training of dehazing network while negative information is unexploited. Moreover, most of them focus on str... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Contrastive_Learning_for_Compact_Single_Image_Dehazing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Contrastive_Learning_for_CVPR_2021_supplemental.pdf | null |
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors | Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, Yizhou Yu | Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the region proposal mechanisms and ROI-based instance-level features to design fine-grained feature alignment modules with respect to the foreground... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13757 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_I3Net_Implicit_Instance-Invariant_Network_for_Adapting_One-Stage_Object_Detectors_CVPR_2021_paper.html | CVPR 2021 | null | null |
Body Meshes as Points | Jianfeng Zhang, Dongdong Yu, Jun Hao Liew, Xuecheng Nie, Jiashi Feng | We consider the challenging multi-person 3D body mesh estimation task in this work. Existing methods are mostly two-stage based--one stage for person localization and the other stage for individual body mesh estimation, leading to redundant pipelines with high computation cost and degraded performance for complex scene... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.02467 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Body_Meshes_as_CVPR_2021_supplemental.pdf | null |
Pixel-Aligned Volumetric Avatars | Amit Raj, Michael Zollhofer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi | Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, s... | https://openaccess.thecvf.com/content/CVPR2021/papers/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Raj_Pixel-Aligned_Volumetric_Avatars_CVPR_2021_supplemental.zip | null |
UC2: Universal Cross-Lingual Cross-Modal Vision-and-Language Pre-Training | Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu, Jingjing Liu | Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC^2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00332 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_UC2_Universal_Cross-Lingual_Cross-Modal_Vision-and-Language_Pre-Training_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_UC2_Universal_Cross-Lingual_CVPR_2021_supplemental.pdf | null |
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification | Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu | We propose a generative model of unordered point sets, such as point clouds, in the forms of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual poi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.01301 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | CVPR 2021 | null | null |
Blur, Noise, and Compression Robust Generative Adversarial Networks | Takuhiro Kaneko, Tatsuya Harada | Generative adversarial networks (GANs) have gained considerable attention owing to their ability to reproduce images. However, they can recreate training images faithfully despite image degradation in the form of blur, noise, and compression, generating similarly degraded images. To solve this problem, the recently pro... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.07849 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kaneko_Blur_Noise_and_Compression_Robust_Generative_Adversarial_Networks_CVPR_2021_paper.html | CVPR 2021 | null | null |
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect | Athena Sayles, Ashish Hooda, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes | Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can se... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sayles_Invisible_Perturbations_Physical_Adversarial_Examples_Exploiting_the_Rolling_Shutter_Effect_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sayles_Invisible_Perturbations_Physical_CVPR_2021_supplemental.pdf | null |
Introvert: Human Trajectory Prediction via Conditional 3D Attention | Nasim Shafiee, Taskin Padir, Ehsan Elhamifar | Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. Human trajectories are affected by both the physical features of the environment and social interactions with other humans. Despite recent surge of studies on human path prediction, most ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shafiee_Introvert_Human_Trajectory_Prediction_via_Conditional_3D_Attention_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shafiee_Introvert_Human_Trajectory_CVPR_2021_supplemental.pdf | null |
Camouflaged Object Segmentation With Distraction Mining | Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, Deng-Ping Fan | Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key challenge of COS is that there exist high intrinsic similarities between the candidate objects and noise background. In this paper, we strive t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Camouflaged_Object_Segmentation_With_Distraction_Mining_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mei_Camouflaged_Object_Segmentation_CVPR_2021_supplemental.pdf | null |
RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction | Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Niessner | Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. Previous works often convert point cloud into regular grids (e.g. voxels or bird-eye view images), and resort to grid-based convolutions for scene understanding. In this work... | https://openaccess.thecvf.com/content/CVPR2021/papers/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Nie_RfD-Net_Point_Scene_Understanding_by_Semantic_Instance_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Nie_RfD-Net_Point_Scene_CVPR_2021_supplemental.pdf | null |
In the Light of Feature Distributions: Moment Matching for Neural Style Transfer | Nikolai Kalischek, Jan D. Wegner, Konrad Schindler | Style transfer aims to render the content of a given image in the graphical/artistic style of another image. The fundamental concept underlying Neural Style Transfer (NST) is to interpret style as a distribution in the feature space of a Convolutional Neural Network, such that a desired style can be achieved by matchin... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kalischek_In_the_Light_of_Feature_Distributions_Moment_Matching_for_Neural_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kalischek_In_the_Light_CVPR_2021_supplemental.pdf | null |
DOTS: Decoupling Operation and Topology in Differentiable Architecture Search | Yu-Chao Gu, Li-Juan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, Ming-Ming Cheng | Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2010.00969 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gu_DOTS_Decoupling_Operation_and_Topology_in_Differentiable_Architecture_Search_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gu_DOTS_Decoupling_Operation_CVPR_2021_supplemental.pdf | null |
DriveGAN: Towards a Controllable High-Quality Neural Simulation | Seung Wook Kim, Jonah Philion, Antonio Torralba, Sanja Fidler | Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to use machine learning to learn how the environment behaves in response to an action, directly from data. In this work, we aim to learn to si... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.15060 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_DriveGAN_Towards_a_Controllable_High-Quality_Neural_Simulation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_DriveGAN_Towards_a_CVPR_2021_supplemental.zip | null |
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer | Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan | Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). Although appealing results have been widely reported in literature, our empirical studies on four well-known AST approaches (GoogleMagenta, AdaIN, LinearTransfer, and SANet) show tha... | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Style-Aware_Normalized_Loss_for_Improving_Arbitrary_Style_Transfer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cheng_Style-Aware_Normalized_Loss_CVPR_2021_supplemental.pdf | null |
Wide-Depth-Range 6D Object Pose Estimation in Space | Yinlin Hu, Sebastien Speierer, Wenzel Jakob, Pascal Fua, Mathieu Salzmann | 6D pose estimation in space poses unique challenges that are not commonly encountered in the terrestrial setting. One of the most striking differences is the lack of atmospheric scattering, allowing objects to be visible from a great distance while complicating illumination conditions. Currently available benchmark dat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Wide-Depth-Range_6D_Object_Pose_Estimation_in_Space_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Salient Boundary Feature for Anchor-free Temporal Action Localization | Chuming Lin, Chengming Xu, Donghao Luo, Yabiao Wang, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Yanwei Fu | Temporal action localization is an important yet challenging task in video understanding. Typically, such a task aims at inferring both the action category and localization of the start and end frame for each action instance in a long, untrimmed video. While most current models achieve good results by using pre-defined... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Learning_Salient_Boundary_Feature_for_Anchor-free_Temporal_Action_Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Learning_Salient_Boundary_CVPR_2021_supplemental.pdf | null |
Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model | Julian Lienen, Eyke Hullermeier, Ralph Ewerth, Nils Nommensen | In many real-world applications, the relative depth of objects in an image is crucial for scene understanding. Recent approaches mainly tackle the problem of depth prediction in monocular images by treating the problem as a regression task. Yet, being interested in an order relation in the first place, ranking methods ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lienen_Monocular_Depth_Estimation_via_Listwise_Ranking_Using_the_Plackett-Luce_Model_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lienen_Monocular_Depth_Estimation_CVPR_2021_supplemental.pdf | null |
Holistic 3D Scene Understanding From a Single Image With Implicit Representation | Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, Shuaicheng Liu | We present a new pipeline for holistic 3D scene understanding from a single image, which could predict object shape, object pose and scene layout. As it is a highly ill-posed problem, existing methods usually suffer from inaccurate estimation of both shapes and layout especially for the cluttered scene due to the heavy... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06422 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Holistic_3D_Scene_Understanding_From_a_Single_Image_With_Implicit_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Holistic_3D_Scene_CVPR_2021_supplemental.pdf | null |
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization | Jiahui Huang, He Wang, Tolga Birdal, Minhyuk Sung, Federica Arrigoni, Shi-Min Hu, Leonidas J. Guibas | We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency acro... | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MultiBodySync_Multi-Body_Segmentation_CVPR_2021_supplemental.pdf | null |
Learning Optical Flow From a Few Matches | Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley | State-of-the-art neural network models for optical flow estimation require a dense correlation volume at high resolutions for representing per-pixel displacement. Although the dense correlation volume is informative for accurate estimation, its heavy computation and memory usage hinders the efficient training and deplo... | https://openaccess.thecvf.com/content/CVPR2021/papers/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02166 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jiang_Learning_Optical_Flow_From_a_Few_Matches_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learnable Motion Coherence for Correspondence Pruning | Yuan Liu, Lingjie Liu, Cheng Lin, Zhen Dong, Wenping Wang | Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14563 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Learnable_Motion_Coherence_for_Correspondence_Pruning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Learnable_Motion_Coherence_CVPR_2021_supplemental.pdf | null |
ManipulaTHOR: A Framework for Visual Object Manipulation | Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi | The domain of Embodied AI has recently witnessed substantial progress, particularly in navigating agents within their environments. These early successes have laid the building blocks for the community to tackle tasks that require agents to actively interact with objects in their environment. Object manipulation is an ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.11213 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ehsani_ManipulaTHOR_A_Framework_for_Visual_Object_Manipulation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ehsani_ManipulaTHOR_A_Framework_CVPR_2021_supplemental.zip | null |
DeepI2P: Image-to-Point Cloud Registration via Deep Classification | Jiaxin Li, Gim Hee Lee | This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation bet... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_DeepI2P_Image-to-Point_Cloud_CVPR_2021_supplemental.pdf | null |
Scene-Intuitive Agent for Remote Embodied Visual Grounding | Xiangru Lin, Guanbin Li, Yizhou Yu | Humans learn from life events to form intuitions towards the understanding of visual environments and languages. Envision that you are instructed by a high-level instruction, "Go to the bathroom in the master bedroom and replace the blue towel on the left wall", what would you possibly do to carry out the task? Intuiti... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lin_Scene-Intuitive_Agent_for_Remote_Embodied_Visual_Grounding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lin_Scene-Intuitive_Agent_for_CVPR_2021_supplemental.pdf | null |
Human-Like Controllable Image Captioning With Verb-Specific Semantic Roles | Long Chen, Zhihong Jiang, Jun Xiao, Wei Liu | Controllable Image Captioning (CIC) -- generating image descriptions following designated control signals -- has received unprecedented attention over the last few years. To emulate the human ability in controlling caption generation, current CIC studies focus exclusively on control signals concerning objective propert... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.12204 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Human-Like_Controllable_Image_Captioning_With_Verb-Specific_Semantic_Roles_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Human-Like_Controllable_Image_CVPR_2021_supplemental.pdf | null |
Enhancing the Transferability of Adversarial Attacks Through Variance Tuning | Xiaosen Wang, Kun He | Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak transferability in the black-box setting, especially under the sc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Enhancing_the_Transferability_of_Adversarial_Attacks_Through_Variance_Tuning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Enhancing_the_Transferability_CVPR_2021_supplemental.pdf | null |
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms | Mahmoud Afifi, Marcus A. Brubaker, Michael S. Brown | While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11731 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_HistoGAN_Controlling_Colors_of_GAN-Generated_and_Real_Images_via_Color_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Afifi_HistoGAN_Controlling_Colors_CVPR_2021_supplemental.pdf | null |
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification | Ruibing Hou, Hong Chang, Bingpeng Ma, Rui Huang, Shiguang Shan | In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to pres... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_BiCnet-TKS_Learning_Efficient_Spatial-Temporal_Representation_for_Video_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Probabilistic Model Distillation for Semantic Correspondence | Xin Li, Deng-Ping Fan, Fan Yang, Ao Luo, Hong Cheng, Zicheng Liu | Semantic correspondence is a fundamental problem in computer vision, which aims at establishing dense correspondences across images depicting different instances under the same category. This task is challenging due to large intra-class variations and a severe lack of ground truth. A popular solution is to learn corres... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Probabilistic_Model_Distillation_for_Semantic_Correspondence_CVPR_2021_paper.html | CVPR 2021 | null | null |
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets | Zhengqin Li, Ting-Wei Yu, Shen Sang, Sarah Wang, Meng Song, Yuhan Liu, Yu-Ying Yeh, Rui Zhu, Nitesh Gundavarapu, Jia Shi, Sai Bi, Hong-Xing Yu, Zexiang Xu, Kalyan Sunkavalli, Milos Hasan, Ravi Ramamoorthi, Manmohan Chandraker | We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, allowing researchers to transform scans into datasets with highquality ground truth. We demons... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_OpenRooms_An_Open_Framework_for_Photorealistic_Indoor_Scene_Datasets_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_OpenRooms_An_Open_CVPR_2021_supplemental.pdf | null |
SSAN: Separable Self-Attention Network for Video Representation Learning | Xudong Guo, Xun Guo, Yan Lu | Self-attention has been successfully applied to video representation learning due to the effectiveness of modeling long range dependencies. Existing approaches build the dependencies merely by computing the pairwise correlations along spatial and temporal dimensions simultaneously. However, spatial correlations and tem... | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.13033 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_SSAN_Separable_Self-Attention_Network_for_Video_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
4D Panoptic LiDAR Segmentation | Mehmet Aygun, Aljosa Osep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixe | Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an approach and a novel eva... | https://openaccess.thecvf.com/content/CVPR2021/papers/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.12472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Aygun_4D_Panoptic_LiDAR_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aygun_4D_Panoptic_LiDAR_CVPR_2021_supplemental.pdf | null |
SceneGen: Learning To Generate Realistic Traffic Scenes | Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun | We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synt... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_SceneGen_Learning_To_CVPR_2021_supplemental.pdf | null |
Natural Adversarial Examples | Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song | We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.pdf | http://arxiv.org/abs/1907.07174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hendrycks_Natural_Adversarial_Examples_CVPR_2021_supplemental.pdf | null |
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models | Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang | Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors wit... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.08697 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_CausalVAE_Disentangled_Representation_Learning_via_Neural_Structural_Causal_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_CausalVAE_Disentangled_Representation_CVPR_2021_supplemental.pdf | null |
VideoMoCo: Contrastive Video Representation Learning With Temporally Adversarial Examples | Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, Wei Liu | MoCo is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning. Given a video sequence as an input sample, we improve the temporal feature representations of MoCo from two perspectives. First, we introduce a generator to drop out seve... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_VideoMoCo_Contrastive_Video_Representation_Learning_With_Temporally_Adversarial_Examples_CVPR_2021_paper.html | CVPR 2021 | null | null |
Zero-Shot Instance Segmentation | Ye Zheng, Jiahong Wu, Yongqiang Qin, Faen Zhang, Li Cui | Deep learning has significantly improved the precision of instance segmentation with abundant labeled data. However, in many areas like medical and manufacturing, collecting sufficient data is extremely hard and labeling this data requires high professional skills. We follow this motivation and propose a new task set n... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Zero-Shot_Instance_Segmentation_CVPR_2021_supplemental.pdf | null |
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes | Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll | Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF, are designed to learn a single scene with a neural network and require dense multi-view inputs. Testing on a new scene requir... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06935 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chibane_Stereo_Radiance_Fields_SRF_Learning_View_Synthesis_for_Sparse_Views_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chibane_Stereo_Radiance_Fields_CVPR_2021_supplemental.pdf | null |
Global Transport for Fluid Reconstruction With Learned Self-Supervision | Erik Franz, Barbara Solenthaler, Nils Thuerey | We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition we introduce a learned self-supervision that constrains observations fr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Franz_Global_Transport_for_Fluid_Reconstruction_With_Learned_Self-Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Franz_Global_Transport_for_CVPR_2021_supplemental.pdf | null |
SliceNet: Deep Dense Depth Estimation From a Single Indoor Panorama Using a Slice-Based Representation | Giovanni Pintore, Marco Agus, Eva Almansa, Jens Schneider, Enrico Gobbetti | We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360 images. Starting from the fact that gravity plays an important role in the design and construction of man-made i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pintore_SliceNet_Deep_Dense_Depth_Estimation_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pintore_SliceNet_Deep_Dense_CVPR_2021_supplemental.pdf | null |
Offboard 3D Object Detection From Point Cloud Sequences | Charles R. Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, Dragomir Anguelov | While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels. Existing 3D object detectors fail to satisfy the high-quality requirem... | https://openaccess.thecvf.com/content/CVPR2021/papers/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05073 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qi_Offboard_3D_Object_Detection_From_Point_Cloud_Sequences_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qi_Offboard_3D_Object_CVPR_2021_supplemental.zip | null |
STaR: Self-Supervised Tracking and Reconstruction of Rigid Objects in Motion With Neural Rendering | Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove | We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned fu... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.01602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_STaR_Self-Supervised_Tracking_and_Reconstruction_of_Rigid_Objects_in_Motion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yuan_STaR_Self-Supervised_Tracking_CVPR_2021_supplemental.zip | null |
Generalization on Unseen Domains via Inference-Time Label-Preserving Target Projections | Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP | Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem. While many approaches have been proposed to solve this problem, they only utilize source data during training, but do not take advantage of the fact that a single ta... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pandey_Generalization_on_Unseen_Domains_via_Inference-Time_Label-Preserving_Target_Projections_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pandey_Generalization_on_Unseen_CVPR_2021_supplemental.pdf | null |
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach | Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, Qinhong Jiang | Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common due to the slight fluctuation of road smoothness and slope. Due to the lack of insight in industrial application, existing methods on open dat... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_3D_Object_Detection_An_Extrinsic_Parameter_Free_Approach_CVPR_2021_paper.html | CVPR 2021 | null | null |
Communication Efficient SGD via Gradient Sampling With Bayes Prior | Liuyihan Song, Kang Zhao, Pan Pan, Yu Liu, Yingya Zhang, Yinghui Xu, Rong Jin | Gradient compression has been widely adopted in data-parallel distributed training of deep neural networks to reduce communication overhead. Some literatures have demonstrated that large gradients are more important than small ones because they contain more information, such as Top-k compressor. Other mainstream method... | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Communication_Efficient_SGD_via_Gradient_Sampling_With_Bayes_Prior_CVPR_2021_paper.html | CVPR 2021 | null | null |
AdaBins: Depth Estimation Using Adaptive Bins | Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka | We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a... | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Bhat_AdaBins_Depth_Estimation_Using_Adaptive_Bins_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhat_AdaBins_Depth_Estimation_CVPR_2021_supplemental.zip | null |
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data | Wenyu Li, Tianchu Guo, Pengyu Li, Binghui Chen, Biao Wang, Wangmeng Zuo, Lei Zhang | Recently, exploiting the effect of the unlabeled data for face recognition attracts increasing attention. However, there are still few works considering the situation that the unlabeled data is shallow which widely exists in real-world scenarios. The existing semi-supervised face recognition methods which focus on gene... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_VirFace_Enhancing_Face_Recognition_via_Unlabeled_Shallow_Data_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_VirFace_Enhancing_Face_CVPR_2021_supplemental.pdf | null |
Pulsar: Efficient Sphere-Based Neural Rendering | Christoph Lassner, Michael Zollhofer | We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end tr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.07484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lassner_Pulsar_Efficient_Sphere-Based_Neural_Rendering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lassner_Pulsar_Efficient_Sphere-Based_CVPR_2021_supplemental.pdf | null |
Contrastive Learning Based Hybrid Networks for Long-Tailed Image Classification | Peng Wang, Kai Han, Xiu-Shen Wei, Lei Zhang, Lei Wang | Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrast... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Contrastive_Learning_Based_Hybrid_Networks_for_Long-Tailed_Image_Classification_CVPR_2021_paper.html | CVPR 2021 | null | null |
Visualizing Adapted Knowledge in Domain Transfer | Yunzhong Hou, Liang Zheng | A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Visualizing_Adapted_Knowledge_in_Domain_Transfer_CVPR_2021_paper.html | CVPR 2021 | null | null |
Delving into Data: Effectively Substitute Training for Black-box Attack | Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, Xiangyang Xue | Deep models have shown their vulnerability when processing adversarial samples. As for the black-box attack, without access to the architecture and weights of the attacked model, training a substitute model for adversarial attacks has attracted wide attention. Previous substitute training approaches focus on stealing t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.12378 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Delving_into_Data_Effectively_Substitute_Training_for_Black-box_Attack_CVPR_2021_paper.html | CVPR 2021 | null | null |
How To Exploit the Transferability of Learned Image Compression to Conventional Codecs | Jan P. Klopp, Keng-Chi Liu, Liang-Gee Chen, Shao-Yi Chien | Lossy image compression is often limited by the simplicity of the chosen loss measure. Recent research suggests that generative adversarial networks have the ability to overcome this limitation and serve as a multi-modal loss, especially for textures. Together with learned image compression, these two techniques can be... | https://openaccess.thecvf.com/content/CVPR2021/papers/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.01874 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Klopp_How_To_Exploit_the_Transferability_of_Learned_Image_Compression_to_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Klopp_How_To_Exploit_CVPR_2021_supplemental.pdf | null |
CorrNet3D: Unsupervised End-to-End Learning of Dense Correspondence for 3D Point Clouds | Yiming Zeng, Yue Qian, Zhiyu Zhu, Junhui Hou, Hui Yuan, Ying He | Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -the first unsupervised and end-to-end deep learning-based framework - to drive the learning of dense correspondence between 3D shapes by means of deformatio... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15638 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zeng_CorrNet3D_Unsupervised_End-to-End_Learning_of_Dense_Correspondence_for_3D_Point_CVPR_2021_paper.html | CVPR 2021 | null | null |
Single-View Robot Pose and Joint Angle Estimation via Render & Compare | Yann Labbe, Justin Carpentier, Mathieu Aubry, Josef Sivic | We introduce RoboPose, a method to estimate the joint angles and the 6D camera-to-robot pose of a known articulated robot from a single RGB image. This is an important problem to grant mobile and itinerant autonomous systems the ability to interact with other robots using only visual information in non-instrumented env... | https://openaccess.thecvf.com/content/CVPR2021/papers/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Labbe_Single-View_Robot_Pose_and_Joint_Angle_Estimation_via_Render__CVPR_2021_paper.html | CVPR 2021 | null | null |
Harmonious Semantic Line Detection via Maximal Weight Clique Selection | Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Chang-Su Kim | A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal proc... | https://openaccess.thecvf.com/content/CVPR2021/papers/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jin_Harmonious_Semantic_Line_Detection_via_Maximal_Weight_Clique_Selection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jin_Harmonious_Semantic_Line_CVPR_2021_supplemental.zip | null |
Learning the Non-Differentiable Optimization for Blind Super-Resolution | Zheng Hui, Jie Li, Xiumei Wang, Xinbo Gao | Previous convolutional neural network (CNN) based blind super-resolution (SR) methods usually adopt an iterative optimization way to approximate the ground-truth (GT) step-by-step. This solution always involves more computational costs to bring about time-consuming inference. At present, most blind SR algorithms are de... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hui_Learning_the_Non-Differentiable_Optimization_for_Blind_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hui_Learning_the_Non-Differentiable_CVPR_2021_supplemental.pdf | null |
Progressive Temporal Feature Alignment Network for Video Inpainting | Xueyan Zou, Linjie Yang, Ding Liu, Yong Jae Lee | Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolut... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.03507 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zou_Progressive_Temporal_Feature_Alignment_Network_for_Video_Inpainting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zou_Progressive_Temporal_Feature_CVPR_2021_supplemental.pdf | null |
Bottleneck Transformers for Visual Recognition | Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani | We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.11605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Srinivas_Bottleneck_Transformers_for_Visual_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Srinivas_Bottleneck_Transformers_for_CVPR_2021_supplemental.pdf | null |
Calibrated RGB-D Salient Object Detection | Wei Ji, Jingjing Li, Shuang Yu, Miao Zhang, Yongri Piao, Shunyu Yao, Qi Bi, Kai Ma, Yefeng Zheng, Huchuan Lu, Li Cheng | Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling | Ze Yang, Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, Raquel Urtasun | Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation. As there are exponentially many variations of humans with different shape, pose and clothing, it is critical to develop methods that can au... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.06571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_S3_Neural_Shape_Skeleton_and_Skinning_Fields_for_3D_Human_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_S3_Neural_Shape_CVPR_2021_supplemental.zip | null |
OSTeC: One-Shot Texture Completion | Baris Gecer, Jiankang Deng, Stefanos Zafeiriou | The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Ad... | https://openaccess.thecvf.com/content/CVPR2021/papers/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.15370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gecer_OSTeC_One-Shot_Texture_CVPR_2021_supplemental.pdf | null |
Learning To Count Everything | Viresh Ranjan, Udbhav Sharma, Thu Nguyen, Minh Hoai | Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-sh... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ranjan_Learning_To_Count_Everything_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ranjan_Learning_To_Count_CVPR_2021_supplemental.pdf | null |
Robust Representation Learning With Feedback for Single Image Deraining | Chenghao Chen, Hao Li | A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.12463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html | CVPR 2021 | null | null |
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction | Feng Liu, Luan Tran, Xiaoming Liu | Inferring 3D structure of a generic object from a 2D image is a long-standing objective of computer vision. Conventional approaches either learn completely from CAD-generated synthetic data, which have difficulty in inference from real images, or generate 2.5D depth image via intrinsic decomposition, which is limited c... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Fully_Understanding_Generic_Objects_Modeling_Segmentation_and_Reconstruction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Fully_Understanding_Generic_CVPR_2021_supplemental.zip | null |
SSN: Soft Shadow Network for Image Compositing | Yichen Sheng, Jianming Zhang, Bedrich Benes | We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such as angle and softnes... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2007.08211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sheng_SSN_Soft_Shadow_Network_for_Image_Compositing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sheng_SSN_Soft_Shadow_CVPR_2021_supplemental.zip | null |
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection | Jia-Chang Feng, Fa-Ting Hong, Wei-Shi Zheng | Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST) to efficiently refine task-specifi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01633 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Feng_MIST_Multiple_Instance_Self-Training_Framework_for_Video_Anomaly_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Feng_MIST_Multiple_Instance_CVPR_2021_supplemental.pdf | null |
VinVL: Revisiting Visual Representations in Vision-Language Models | Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao | This paper presents a detailed study of improving vision features and develops an improved object detection model for vision language (VL) tasks. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, pre-trained on much larger training corpora that combine multiple public annotated... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.00529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VinVL_Revisiting_Visual_Representations_in_Vision-Language_Models_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_VinVL_Revisiting_Visual_CVPR_2021_supplemental.pdf | null |
Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression | Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang | In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representation... | https://openaccess.thecvf.com/content/CVPR2021/papers/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Geng_Bottom-Up_Human_Pose_Estimation_via_Disentangled_Keypoint_Regression_CVPR_2021_paper.html | CVPR 2021 | null | null |
CoMoGAN: Continuous Model-Guided Image-to-Image Translation | Fabio Pizzati, Pietro Cerri, Raoul de Charette | CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. To that matter, we introduce a new Functional Instance Normalization layer and residual mechanism, which together disentangle image content from position on target manifold. We rely on naive physics-inspi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pizzati_CoMoGAN_Continuous_Model-Guided_Image-to-Image_Translation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pizzati_CoMoGAN_Continuous_Model-Guided_CVPR_2021_supplemental.zip | null |
Self-Supervised Video Hashing via Bidirectional Transformers | Shuyan Li, Xiu Li, Jiwen Lu, Jie Zhou | Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on B... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Self-Supervised_Video_Hashing_via_Bidirectional_Transformers_CVPR_2021_paper.html | CVPR 2021 | null | null |
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation | Chen Li, Gim Hee Lee | Animal pose estimation is an important field that has received increasing attention in the recent years. The main challenge for this task is the lack of labeled data. Existing works circumvent this problem with pseudo labels generated from data of other easily accessible domains such as synthetic data. However, these p... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_From_Synthetic_to_Real_Unsupervised_Domain_Adaptation_for_Animal_Pose_CVPR_2021_paper.html | CVPR 2021 | null | null |
Safe Local Motion Planning With Self-Supervised Freespace Forecasting | Peiyun Hu, Aaron Huang, John Dolan, David Held, Deva Ramanan | Safe local motion planning for autonomous driving in dynamic environments requires forecasting how the scene evolves. Practical autonomy stacks adopt a semantic object-centric representation of a dynamic scene and build object detection, tracking, and prediction modules to solve forecasting. However, training these mod... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Safe_Local_Motion_CVPR_2021_supplemental.zip | null |
End of preview. Expand in Data Studio
CVPR 2021 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2021 Open Access website (https://openaccess.thecvf.com/CVPR2021) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2021). For researchers who are interested in doing analysis of CVPR 2021 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification",
"authors": "Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, Yi Shan",
"abstract": "Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Dual_Cross-Attention_Learning_CVPR_2022_supplemental.pdf",
"arXiv": "http://arxiv.org/abs/2205.02151",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html",
"tags": "CVPR 2022"
}
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