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title string | authors string | abstract string | pdf string | arXiv string | bibtex string | url string | detail_url string | tags string | supp string | string |
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Learning Student Networks in the Wild | Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang | Data-free learning for student networks is a new paradigm for solving users' anxiety caused by the privacy problem of using original training data. Since the architectures of modern convolutional neural networks (CNNs) are compact and sophisticated, the alternative images or meta-data generated from the teacher network... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | null | null |
Distilling Knowledge via Knowledge Review | Pengguang Chen, Shu Liu, Hengshuang Zhao, Jiaya Jia | Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods mostly focus on proposing feature transformation and loss functions between the same level's features to improve the effectiveness. We diffe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.09044 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Distilling_Knowledge_via_Knowledge_Review_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Distilling_Knowledge_via_CVPR_2021_supplemental.pdf | null |
DoDNet: Learning To Segment Multi-Organ and Tumors From Multiple Partially Labeled Datasets | Jianpeng Zhang, Yutong Xie, Yong Xia, Chunhua Shen | Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel level, most benchmark datasets are equipped with the annotations of only one type of organs and/or tumors, resulting in the so-called partially labeling issue. To address this issue, we propose a dynamic on-demand network (DoDNe... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DoDNet_Learning_To_Segment_Multi-Organ_and_Tumors_From_Multiple_Partially_CVPR_2021_paper.html | CVPR 2021 | null | null |
Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection | Alexandros Haliassos, Konstantinos Vougioukas, Stavros Petridis, Maja Pantic | Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operation... | https://openaccess.thecvf.com/content/CVPR2021/papers/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Haliassos_Lips_Dont_Lie_CVPR_2021_supplemental.pdf | null |
Exploring Simple Siamese Representation Learning | Xinlei Chen, Kaiming He | Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results th... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.10566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Exploring_Simple_Siamese_CVPR_2021_supplemental.pdf | null |
CAMERAS: Enhanced Resolution and Sanity Preserving Class Activation Mapping for Image Saliency | Mohammad A. A. K. Jalwana, Naveed Akhtar, Mohammed Bennamoun, Ajmal Mian | Backpropagation image saliency aims at explaining model predictions by estimating model-centric importance of individual pixels in the input. However, class-insensitivity of the earlier layers in a network only allows saliency computation with low resolution activation maps of the deeper layers, resulting in compromise... | https://openaccess.thecvf.com/content/CVPR2021/papers/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.10649 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Jalwana_CAMERAS_Enhanced_Resolution_and_Sanity_Preserving_Class_Activation_Mapping_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jalwana_CAMERAS_Enhanced_Resolution_CVPR_2021_supplemental.pdf | null |
3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding | Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, Kui Jia | The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly fun... | https://openaccess.thecvf.com/content/CVPR2021/papers/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16397 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Deng_3D_AffordanceNet_A_CVPR_2021_supplemental.pdf | null |
Learning To Segment Actions From Visual and Language Instructions via Differentiable Weak Sequence Alignment | Yuhan Shen, Lu Wang, Ehsan Elhamifar | We address the problem of unsupervised localization of key-steps and feature learning in instructional videos using both visual and language instructions. Our key observation is that the sequences of visual and linguistic key-steps are weakly aligned: there is an ordered one-to-one correspondence between most visual an... | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_Learning_To_Segment_Actions_From_Visual_and_Language_Instructions_via_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Shen_Learning_To_Segment_CVPR_2021_supplemental.pdf | null |
Deep Implicit Templates for 3D Shape Representation | Zerong Zheng, Tao Yu, Qionghai Dai, Yebin Liu | Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationshi... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14565 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zheng_Deep_Implicit_Templates_CVPR_2021_supplemental.zip | null |
Semantic Image Matting | Yanan Sun, Chi-Keung Tang, Yu-Wing Tai | Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases,... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Semantic_Image_Matting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.08201 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Semantic_Image_Matting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Semantic_Image_Matting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Semantic_Image_Matting_CVPR_2021_supplemental.pdf | null |
Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision | Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang | In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision (CPS). Our approach imposes the consistency on two segmentation networks perturbed with different in... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.01226 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Semantic_Segmentation_With_Cross_Pseudo_Supervision_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semi-Supervised_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Ranking Neural Checkpoints | Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong | This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task. Thanks to the broad use of DNNs, we may easily collect hundreds of checkpoints from various sources. Which of them transfers the best to our downstream task of interest?... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.11200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Ranking_Neural_Checkpoints_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Ranking_Neural_Checkpoints_CVPR_2021_supplemental.pdf | null |
SuperMix: Supervising the Mixing Data Augmentation | Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi | This paper presents a supervised mixing augmentation method termed SuperMix, which exploits the salient regions within input images to construct mixed training samples. SuperMix is designed to obtain mixed images rich in visual features and complying with realistic image priors. To enhance the efficiency of the algorit... | https://openaccess.thecvf.com/content/CVPR2021/papers/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.05034 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dabouei_SuperMix_Supervising_the_Mixing_Data_Augmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dabouei_SuperMix_Supervising_the_CVPR_2021_supplemental.pdf | null |
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection | Luwei Hou, Yu Zhang, Kui Fu, Jia Li | Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i.e. with object bounding boxes) to train object detectors for target domains that are weakly labeled (i.e. with image-level tags). Instead of domain-level distribution matching, as popularly... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Informative_and_Consistent_Correspondence_Mining_for_Cross-Domain_Weakly_Supervised_Object_CVPR_2021_paper.html | CVPR 2021 | null | null |
Inception Convolution With Efficient Dilation Search | Jie Liu, Chuming Li, Feng Liang, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang, Dong Xu | As a variant of standard convolution, a dilated convolution can control effective receptive fields and handle large scale variance of objects without introducing additional computational costs. To fully explore the potential of dilated convolution, we proposed a new type of dilated convolution (referred to as inception... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.13587 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Inception_Convolution_With_Efficient_Dilation_Search_CVPR_2021_paper.html | CVPR 2021 | null | null |
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy | Federico Paredes-Valles, Guido C. H. E. de Croon | Event cameras are novel vision sensors that sample, in an asynchronous fashion, brightness increments with low latency and high temporal resolution. The resulting streams of events are of high value by themselves, especially for high speed motion estimation. However, a growing body of work has also focused on the recon... | https://openaccess.thecvf.com/content/CVPR2021/papers/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Paredes-Valles_Back_to_Event_Basics_Self-Supervised_Learning_of_Image_Reconstruction_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Paredes-Valles_Back_to_Event_CVPR_2021_supplemental.pdf | null |
AdderSR: Towards Energy Efficient Image Super-Resolution | Dehua Song, Yunhe Wang, Hanting Chen, Chang Xu, Chunjing Xu, Dacheng Tao | This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inh... | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.08891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_AdderSR_Towards_Energy_Efficient_Image_Super-Resolution_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_AdderSR_Towards_Energy_CVPR_2021_supplemental.pdf | null |
Semi-Supervised Domain Adaptation Based on Dual-Level Domain Mixing for Semantic Segmentation | Shuaijun Chen, Xu Jia, Jianzhong He, Yongjie Shi, Jianzhuang Liu | Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation especially for dense pixel prediction tasks such as semantic segmentation. Recently, both unsupervised domain adaptation (UDA) from large amounts... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04705 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semi-Supervised_Domain_Adaptation_Based_on_Dual-Level_Domain_Mixing_for_Semantic_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semi-Supervised_Domain_Adaptation_CVPR_2021_supplemental.pdf | null |
Connecting What To Say With Where To Look by Modeling Human Attention Traces | Zihang Meng, Licheng Yu, Ning Zhang, Tamara L. Berg, Babak Damavandi, Vikas Singh, Amy Bearman | We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework, where each word of a given caption is paired with a mouse trace segment. We propose two novel tasks: (1) predict a trace given an image and captio... | https://openaccess.thecvf.com/content/CVPR2021/papers/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.05964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Meng_Connecting_What_To_Say_With_Where_To_Look_by_Modeling_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Meng_Connecting_What_To_CVPR_2021_supplemental.pdf | null |
Shelf-Supervised Mesh Prediction in the Wild | Yufei Ye, Shubham Tulsiani, Abhinav Gupta | We aim to infer 3D shape and pose of objects from a single image and propose a learning-based approach that can train from unstructured image collections, using only segmentation outputs from off-the-shelf recognition systems as supervisory signal (i.e. 'shelf-supervised'). We first infer a volumetric representation in... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.pdf | http://arxiv.org/abs/2102.06195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Shelf-Supervised_Mesh_Prediction_in_the_Wild_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Shelf-Supervised_Mesh_Prediction_CVPR_2021_supplemental.pdf | null |
Learning To Filter: Siamese Relation Network for Robust Tracking | Siyuan Cheng, Bineng Zhong, Guorong Li, Xin Liu, Zhenjun Tang, Xianxian Li, Jing Wang | Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00829 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Cheng_Learning_To_Filter_Siamese_Relation_Network_for_Robust_Tracking_CVPR_2021_paper.html | CVPR 2021 | null | null |
Ensembling With Deep Generative Views | Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang | Recent generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose, simply by learning from unlabeled image collections. Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classific... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chai_Ensembling_With_Deep_Generative_Views_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chai_Ensembling_With_Deep_CVPR_2021_supplemental.pdf | null |
Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss | Lu Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang | Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set. However, in reality acquiring massive annotated training data is both expensive and time-consuming. In this paper, we propose a novel two-stage detector for accurate few-shot object d... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Accurate_Few-Shot_Object_CVPR_2021_supplemental.pdf | null |
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding | Yang Zhao, Zhou Zhao, Zhu Zhang, Zhijie Lin | Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of gen... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Cascaded_Prediction_Network_via_Segment_Tree_for_Temporal_Video_Grounding_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Cascaded_Prediction_Network_CVPR_2021_supplemental.pdf | null |
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis | Xianchao Zhang, Ziyang Cheng, Xiaotong Zhang, Han Liu | Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P2GAN), which promotes generator with the real ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Posterior_Promoted_GAN_With_Distribution_Discriminator_for_Unsupervised_Image_Synthesis_CVPR_2021_paper.html | CVPR 2021 | null | null |
Toward Accurate and Realistic Outfits Visualization With Attention to Details | Kedan Li, Min Jin Chong, Jeffrey Zhang, Jingen Liu | Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail to capture ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.06593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Toward_Accurate_and_Realistic_Outfits_Visualization_With_Attention_to_Details_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Toward_Accurate_and_CVPR_2021_supplemental.pdf | null |
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object Segmentation | Haoxin Chen, Hanjie Wu, Nanxuan Zhao, Sucheng Ren, Shengfeng He | This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a man... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Delving_Deep_Into_Many-to-Many_Attention_for_Few-Shot_Video_Object_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Delving_Deep_Into_CVPR_2021_supplemental.pdf | null |
MongeNet: Efficient Sampler for Geometric Deep Learning | Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado | Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary along with a robust distance metric to assess surface quality or as part of the loss function for training models. Current methods often rely on... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.14554 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lebrat_MongeNet_Efficient_Sampler_for_Geometric_Deep_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
Gated Spatio-Temporal Attention-Guided Video Deblurring | Maitreya Suin, A. N. Rajagopalan | Video deblurring remains a challenging task due to the complexity of spatially and temporally varying blur. Most of the existing works depend on implicit or explicit alignment for temporal information fusion, which either increases the computational cost or results in suboptimal performance due to misalignment. In this... | https://openaccess.thecvf.com/content/CVPR2021/papers/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.html | CVPR 2021 | null | null |
Learning Multi-Scale Photo Exposure Correction | Mahmoud Afifi, Konstantinos G. Derpanis, Bjorn Ommer, Michael S. Brown | Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. Exposure problems are categorized as either: (i) overexposed, where the camera exposure was too long, resulting in bright and washed-out image regions, or (ii) underexposed, where the exposure was too short, resulting i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2003.11596 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Afifi_Learning_Multi-Scale_Photo_Exposure_Correction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Afifi_Learning_Multi-Scale_Photo_CVPR_2021_supplemental.pdf | null |
Learning Semantic Person Image Generation by Region-Adaptive Normalization | Zhengyao Lv, Xiaoming Li, Xin Li, Fu Li, Tianwei Lin, Dongliang He, Wangmeng Zuo | Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to transfer the person image from the source to the target pose. However, most of them cannot well capture the semantic appearance, resulting i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06650 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Learning_Semantic_Person_Image_Generation_by_Region-Adaptive_Normalization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Learning_Semantic_Person_CVPR_2021_supplemental.pdf | null |
Rethinking Class Relations: Absolute-Relative Supervised and Unsupervised Few-Shot Learning | Hongguang Zhang, Piotr Koniusz, Songlei Jian, Hongdong Li, Philip H. S. Torr | The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via rel... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2001.03919 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Rethinking_Class_Relations_CVPR_2021_supplemental.pdf | null |
Divergence Optimization for Noisy Universal Domain Adaptation | Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku | Universal domain adaptation (UniDA) has been proposed to transfer knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label sets. In practice, however, it is difficult to obtain a large amount of perfectly clean labeled data in a source domain with limited re... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Divergence_Optimization_for_Noisy_Universal_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Divergence_Optimization_for_CVPR_2021_supplemental.zip | null |
Learning Dynamic Alignment via Meta-Filter for Few-Shot Learning | Chengming Xu, Yanwei Fu, Chen Liu, Chengjie Wang, Jilin Li, Feiyue Huang, Li Zhang, Xiangyang Xue | Few-shot learning (FSL), which aims to recognise new classes by adapting the learned knowledge with extremely limited few-shot (support) examples, remains an important open problem in computer vision. Most of the existing methods for feature alignment in few-shot learning only consider image-level or spatial-level alig... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Learning_Dynamic_Alignment_via_Meta-Filter_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Learning_Dynamic_Alignment_CVPR_2021_supplemental.pdf | null |
Unsupervised Learning of 3D Object Categories From Videos in the Wild | Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny | Recently, numerous works have attempted to learn 3D reconstructors of textured 3D models of visual categories given a training set of annotated static images of objects. In this paper, we seek to decrease the amount of needed supervision by leveraging a collection of object-centric videos captured in-the-wild without r... | https://openaccess.thecvf.com/content/CVPR2021/papers/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Henzler_Unsupervised_Learning_of_3D_Object_Categories_From_Videos_in_the_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Henzler_Unsupervised_Learning_of_CVPR_2021_supplemental.pdf | null |
Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing | Yu Wu, Yi Yang | We investigate the weakly-supervised audio-visual video parsing task, which aims to parse a video into temporal event segments and predict the audible or visible event categories. The task is challenging since there only exist video-level event labels for training, without indicating the temporal boundaries and modalit... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Exploring_Heterogeneous_Clues_for_Weakly-Supervised_Audio-Visual_Video_Parsing_CVPR_2021_paper.html | CVPR 2021 | null | null |
Dogfight: Detecting Drones From Drones Videos | Muhammad Waseem Ashraf, Waqas Sultani, Mubarak Shah | As airborne vehicles are becoming more autonomous and ubiquitous, it has become vital to develop the capability to detect the objects in their surroundings. This paper attempts to address the problem of drones detection from other flying drones. The erratic movement of the source and target drones, small size, arbitrar... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17242 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html | CVPR 2021 | null | null |
PAUL: Procrustean Autoencoder for Unsupervised Lifting | Chaoyang Wang, Simon Lucey | Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer. In this paper we advocate for a 3D deep auto-encoder framework to be used explicitly as the NRSfM prior. The framew... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16773 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_PAUL_Procrustean_Autoencoder_for_Unsupervised_Lifting_CVPR_2021_paper.html | CVPR 2021 | null | null |
Group Collaborative Learning for Co-Salient Object Detection | Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi-Keung Tang, Ling Shao, Yu-Wing Tai | We present a novel group collaborative learning framework (GCNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by... | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.01108 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Group_Collaborative_Learning_for_Co-Salient_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | null | null |
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening | Sungha Choi, Sanghun Jung, Huiwon Yun, Joanne T. Kim, Seungryong Kim, Jaegul Choo | Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unse... | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15597 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_RobustNet_Improving_Domain_CVPR_2021_supplemental.pdf | null |
Monocular Real-Time Full Body Capture With Inter-Part Correlations | Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu | We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike pr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.06087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Monocular_Real-Time_Full_CVPR_2021_supplemental.pdf | null |
Pre-Trained Image Processing Transformer | Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao | As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. The big progress is mainly contributed to the representation ability of transformer and its variant architectur... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00364 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pre-Trained_Image_Processing_Transformer_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Pre-Trained_Image_Processing_CVPR_2021_supplemental.pdf | null |
Robust and Accurate Object Detection via Adversarial Learning | Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong | Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. Noting that most state-of-the-art object detectors benefit from fine-tuning a pre-trained classifier, we first study how the classifiers' gains from various d... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.13886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Robust_and_Accurate_Object_Detection_via_Adversarial_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Robust_and_Accurate_CVPR_2021_supplemental.pdf | null |
Faster Meta Update Strategy for Noise-Robust Deep Learning | Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang | It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training is currently the bottleneck in the meta learning approaches. In this p... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.15092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Faster_Meta_Update_Strategy_for_Noise-Robust_Deep_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Faster_Meta_Update_CVPR_2021_supplemental.pdf | null |
ContactOpt: Optimizing Contact To Improve Grasps | Patrick Grady, Chengcheng Tang, Christopher D. Twigg, Minh Vo, Samarth Brahmbhatt, Charles C. Kemp | Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07267 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Grady_ContactOpt_Optimizing_Contact_To_Improve_Grasps_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Grady_ContactOpt_Optimizing_Contact_CVPR_2021_supplemental.pdf | null |
Panoptic-PolarNet: Proposal-Free LiDAR Point Cloud Panoptic Segmentation | Zixiang Zhou, Yang Zhang, Hassan Foroosh | Panoptic segmentation presents a new challenge in exploiting the merits of both detection and segmentation, with the aim of unifying instance segmentation and semantic segmentation in a single framework. However, an efficient solution for panoptic segmentation in the emerging domain of LiDAR point cloud is still an ope... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_Point_Cloud_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Panoptic-PolarNet_Proposal-Free_LiDAR_CVPR_2021_supplemental.pdf | null |
Source-Free Domain Adaptation for Semantic Segmentation | Yuang Liu, Wei Zhang, Jun Wang | Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network (CNN)-based approaches for semantic segmentation heavily rely on the pixel-level annotated data, which is labor-intensive. However, existing UDA approaches in this regard inevitably require the full access to source datasets... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16372 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Source-Free_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Source-Free_Domain_Adaptation_CVPR_2021_supplemental.pdf | null |
Adaptive Weighted Discriminator for Training Generative Adversarial Networks | Vasily Zadorozhnyy, Qiang Cheng, Qiang Ye | Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends o... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.03149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zadorozhnyy_Adaptive_Weighted_Discriminator_for_Training_Generative_Adversarial_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zadorozhnyy_Adaptive_Weighted_Discriminator_CVPR_2021_supplemental.pdf | null |
Depth From Camera Motion and Object Detection | Brent A. Griffin, Jason J. Corso | This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bo... | https://openaccess.thecvf.com/content/CVPR2021/papers/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.01468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Griffin_Depth_From_Camera_Motion_and_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Griffin_Depth_From_Camera_CVPR_2021_supplemental.pdf | null |
PPR10K: A Large-Scale Portrait Photo Retouching Dataset With Human-Region Mask and Group-Level Consistency | Jie Liang, Hui Zeng, Miaomiao Cui, Xuansong Xie, Lei Zhang | Different from general photo retouching tasks, portrait photo retouching (PPR), which aims to enhance the visual quality of a collection of flat-looking portrait photos, has its special and practical requirements such as human-region priority (HRP) and group-level consistency (GLC). HRP requires that more attention sho... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.09180 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_PPR10K_A_Large-Scale_Portrait_Photo_Retouching_Dataset_With_Human-Region_Mask_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liang_PPR10K_A_Large-Scale_CVPR_2021_supplemental.pdf | null |
Transformation Driven Visual Reasoning | Xin Hong, Yanyan Lan, Liang Pang, Jiafeng Guo, Xueqi Cheng | This paper defines a new visual reasoning paradigm by introducing an important factor, i.e. transformation. The motivation comes from the fact that most existing visual reasoning tasks, such as CLEVR in VQA, are solely defined to test how well the machine understands the concepts and relations within static settings, l... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.13160 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Transformation_Driven_Visual_Reasoning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Transformation_Driven_Visual_CVPR_2021_supplemental.pdf | null |
Sparse R-CNN: End-to-End Object Detection With Learnable Proposals | Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo | We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size HxW. In our method, however, a fixed sparse set of learned object proposals, total leng... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Sparse_R-CNN_End-to-End_Object_Detection_With_Learnable_Proposals_CVPR_2021_paper.html | CVPR 2021 | null | null |
Plan2Scene: Converting Floorplans to 3D Scenes | Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa, Angel X. Chang, Manolis Savva | We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes surface textures based on the input photos; and 3) infers textures for unobserved surfaces ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Vidanapathirana_Plan2Scene_Converting_Floorplans_to_3D_Scenes_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vidanapathirana_Plan2Scene_Converting_Floorplans_CVPR_2021_supplemental.pdf | null |
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges | Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham | An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relatively small spatial scales or have limited semantic annotations du... | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.pdf | http://arxiv.org/abs/2009.03137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Towards_Semantic_Segmentation_of_Urban-Scale_3D_Point_Clouds_A_Dataset_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Towards_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Towards Open World Object Detection | K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian | Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: `Open World Object ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Joseph_Towards_Open_World_Object_Detection_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Joseph_Towards_Open_World_CVPR_2021_supplemental.pdf | null |
Conditional Bures Metric for Domain Adaptation | You-Wei Luo, Chuan-Xian Ren | As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to c... | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Luo_Conditional_Bures_Metric_CVPR_2021_supplemental.pdf | null |
DatasetGAN: Efficient Labeled Data Factory With Minimal Human Effort | Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler | We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time-consuming to annotate. Our method relies on the pow... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.06490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_DatasetGAN_Efficient_Labeled_Data_Factory_With_Minimal_Human_Effort_CVPR_2021_paper.html | CVPR 2021 | null | null |
Repurposing GANs for One-Shot Semantic Part Segmentation | Nontawat Tritrong, Pitchaporn Rewatbowornwong, Supasorn Suwajanakorn | While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? In this work, we test this hypothesis and propose a simple and effective appr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.04379 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tritrong_Repurposing_GANs_for_One-Shot_Semantic_Part_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tritrong_Repurposing_GANs_for_CVPR_2021_supplemental.pdf | null |
Semi-Supervised 3D Hand-Object Poses Estimation With Interactions in Time | Shaowei Liu, Hanwen Jiang, Jiarui Xu, Sifei Liu, Xiaolong Wang | Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unif... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.pdf | http://arxiv.org/abs/2106.05266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Semi-Supervised_3D_Hand-Object_Poses_Estimation_With_Interactions_in_Time_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Semi-Supervised_3D_Hand-Object_CVPR_2021_supplemental.pdf | null |
Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation | Yapeng Tian, Di Hu, Chenliang Xu | There are rich synchronized audio and visual events in our daily life. Inside the events, audio scenes are associated with the corresponding visual objects; meanwhile, sounding objects can indicate and help to separate their individual sounds in the audio track. Based on this observation, in this paper, we propose a cy... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.02026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tian_Cyclic_Co-Learning_of_Sounding_Object_Visual_Grounding_and_Sound_Separation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tian_Cyclic_Co-Learning_of_CVPR_2021_supplemental.pdf | null |
Digital Gimbal: End-to-End Deep Image Stabilization With Learnable Exposure Times | Omer Dahary, Matan Jacoby, Alex M. Bronstein | Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often physically cumbersome and expensive, limiting their widespread use. In this work, we propose to digitally emulate a mechanically stabilized syst... | https://openaccess.thecvf.com/content/CVPR2021/papers/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dahary_Digital_Gimbal_End-to-End_Deep_Image_Stabilization_With_Learnable_Exposure_Times_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dahary_Digital_Gimbal_End-to-End_CVPR_2021_supplemental.pdf | null |
Rethinking Text Segmentation: A Novel Dataset and a Text-Specific Refinement Approach | Xingqian Xu, Zhifei Zhang, Zhaowen Wang, Brian Price, Zhonghao Wang, Humphrey Shi | Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and dedicated investigations, this critical prerequisite has been left as an assumption in many works, and has been largely overlooked by curren... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.14021 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Rethinking_Text_Segmentation_A_Novel_Dataset_and_a_Text-Specific_Refinement_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Rethinking_Text_Segmentation_CVPR_2021_supplemental.pdf | null |
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events | Li Xu, He Huang, Jun Liu | Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collec... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_SUTD-TrafficQA_A_Question_Answering_Benchmark_and_an_Efficient_Network_for_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_SUTD-TrafficQA_A_Question_CVPR_2021_supplemental.pdf | null |
T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval | Xiaohan Wang, Linchao Zhu, Yi Yang | Text-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text-video similarities in a joint embedding space. However, most existing methods only consider the global cross-modal similarity and overlook the local d... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.10054 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_T2VLAD_Global-Local_Sequence_Alignment_for_Text-Video_Retrieval_CVPR_2021_paper.html | CVPR 2021 | null | null |
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings | Mihai Dusmanu, Johannes L. Schonberger, Sudipta N. Sinha, Marc Pollefeys | Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new priva... | https://openaccess.thecvf.com/content/CVPR2021/papers/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.pdf | http://arxiv.org/abs/2006.06634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Dusmanu_Privacy-Preserving_Image_Features_via_Adversarial_Affine_Subspace_Embeddings_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Dusmanu_Privacy-Preserving_Image_Features_CVPR_2021_supplemental.pdf | null |
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval | Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song | Sketch-based image retrieval (SBIR) is a cross-modal matching problem which is typically solved by learning a joint embedding space where the semantic content shared between photo and sketch modalities are preserved. However, a fundamental challenge in SBIR has been largely ignored so far, that is, sketches are drawn b... | https://openaccess.thecvf.com/content/CVPR2021/papers/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15706 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Sain_StyleMeUp_Towards_Style-Agnostic_Sketch-Based_Image_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sain_StyleMeUp_Towards_Style-Agnostic_CVPR_2021_supplemental.pdf | null |
Embedding Transfer With Label Relaxation for Improved Metric Learning | Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak | This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities between samples in the source embedding space as the knowledge, and transfers them through a loss used for learning target embedding models. To t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.14908 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Embedding_Transfer_With_Label_Relaxation_for_Improved_Metric_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Embedding_Transfer_With_CVPR_2021_supplemental.pdf | null |
Beyond Static Features for Temporally Consistent 3D Human Pose and Shape From a Video | Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee | Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed; however, they fail to resolve the single image-based methods' temporal inconsistency... | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.08627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_Beyond_Static_Features_for_Temporally_Consistent_3D_Human_Pose_and_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_Beyond_Static_Features_CVPR_2021_supplemental.zip | null |
Layout-Guided Novel View Synthesis From a Single Indoor Panorama | Jiale Xu, Jia Zheng, Yanyu Xu, Rui Tang, Shenghua Gao | Existing view synthesis methods mainly focus on the perspective images and have shown promising results. However, due to the limited field-of-view of the pinhole camera, the performance quickly degrades when large camera movements are adopted. In this paper, we make the first attempt to generate novel views from a sing... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.17022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Layout-Guided_Novel_View_Synthesis_From_a_Single_Indoor_Panorama_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Layout-Guided_Novel_View_CVPR_2021_supplemental.pdf | null |
STMTrack: Template-Free Visual Tracking With Space-Time Memory Networks | Zhihong Fu, Qingjie Liu, Zehua Fu, Yunhong Wang | Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00324 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_STMTrack_Template-Free_Visual_Tracking_With_Space-Time_Memory_Networks_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_STMTrack_Template-Free_Visual_CVPR_2021_supplemental.pdf | null |
Reformulating HOI Detection As Adaptive Set Prediction | Mingfei Chen, Yue Liao, Si Liu, Zhiyuan Chen, Fei Wang, Chen Qian | Determining which image regions to concentrate is critical for Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on either detected human and object pairs or pre-defined interaction locations, which limits learning of the effective features. In this paper, we reformulate HOI detection as an ada... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.05983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Reformulating_HOI_Detection_As_Adaptive_Set_Prediction_CVPR_2021_paper.html | CVPR 2021 | null | null |
Strengthen Learning Tolerance for Weakly Supervised Object Localization | Guangyu Guo, Junwei Han, Fang Wan, Dingwen Zhang | Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning... | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Strengthen_Learning_Tolerance_for_Weakly_Supervised_Object_Localization_CVPR_2021_paper.html | CVPR 2021 | null | null |
Mesh Saliency: An Independent Perceptual Measure or a Derivative of Image Saliency? | Ran Song, Wei Zhang, Yitian Zhao, Yonghuai Liu, Paul L. Rosin | While mesh saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and is well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art mesh saliency methods remain poor at predicting human fixations. Cues emerging pr... | https://openaccess.thecvf.com/content/CVPR2021/papers/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Song_Mesh_Saliency_An_Independent_Perceptual_Measure_or_a_Derivative_of_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Song_Mesh_Saliency_An_CVPR_2021_supplemental.pdf | null |
Passive Inter-Photon Imaging | Atul Ingle, Trevor Seets, Mauro Buttafava, Shantanu Gupta, Alberto Tosi, Mohit Gupta, Andreas Velten | Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumin... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.00059 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ingle_Passive_Inter-Photon_Imaging_CVPR_2021_supplemental.pdf | null |
Domain Consensus Clustering for Universal Domain Adaptation | Guangrui Li, Guoliang Kang, Yi Zhu, Yunchao Wei, Yi Yang | In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in o... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Domain_Consensus_Clustering_for_Universal_Domain_Adaptation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Domain_Consensus_Clustering_CVPR_2021_supplemental.pdf | null |
Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations | Umberto Michieli, Pietro Zanuttigh | Deep neural networks suffer from the major limitation of catastrophic forgetting old tasks when learning new ones. In this paper we focus on class incremental continual learning in semantic segmentation, where new categories are made available over time while previous training data is not retained. The proposed continu... | https://openaccess.thecvf.com/content/CVPR2021/papers/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.06342 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Michieli_Continual_Semantic_Segmentation_via_Repulsion-Attraction_of_Sparse_and_Disentangled_Latent_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Michieli_Continual_Semantic_Segmentation_CVPR_2021_supplemental.pdf | null |
Audio-Driven Emotional Video Portraits | Xinya Ji, Hang Zhou, Kaisiyuan Wang, Wayne Wu, Chen Change Loy, Xun Cao, Feng Xu | Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotiona... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ji_Audio-Driven_Emotional_Video_Portraits_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ji_Audio-Driven_Emotional_Video_CVPR_2021_supplemental.zip | null |
Pareto Self-Supervised Training for Few-Shot Learning | Zhengyu Chen, Jixie Ge, Heshen Zhan, Siteng Huang, Donglin Wang | While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently drawn much attention to ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.07841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Pareto_Self-Supervised_Training_for_Few-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Pareto_Self-Supervised_Training_CVPR_2021_supplemental.zip | null |
EnD: Entangling and Disentangling Deep Representations for Bias Correction | Enzo Tartaglione, Carlo Alberto Barbano, Marco Grangetto | Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, and nowadays they are used to solve an incredibly large variety of tasks. There are problems, like the presence of biases in the training data, which question the generalization capability of these models. In this work we propose En... | https://openaccess.thecvf.com/content/CVPR2021/papers/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.02023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Tartaglione_EnD_Entangling_and_Disentangling_Deep_Representations_for_Bias_Correction_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tartaglione_EnD_Entangling_and_CVPR_2021_supplemental.pdf | null |
Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising | Tongyao Pang, Huan Zheng, Yuhui Quan, Hui Ji | Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images without ground truth for training. Nevertheless, the performance of th... | https://openaccess.thecvf.com/content/CVPR2021/papers/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_Learning_for_Image_Denoising_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pang_Recorrupted-to-Recorrupted_Unsupervised_Deep_CVPR_2021_supplemental.pdf | null |
Reconsidering Representation Alignment for Multi-View Clustering | Daniel J. Trosten, Sigurd Lokse, Robert Jenssen, Michael Kampffmeyer | Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.07738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Trosten_Reconsidering_Representation_Alignment_for_Multi-View_Clustering_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Trosten_Reconsidering_Representation_Alignment_CVPR_2021_supplemental.pdf | null |
Probabilistic Embeddings for Cross-Modal Retrieval | Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende, Yannis Kalantidis, Diane Larlus | Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particularly challenging. Given an image (respectively a caption), there are m... | https://openaccess.thecvf.com/content/CVPR2021/papers/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.pdf | http://arxiv.org/abs/2101.05068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Chun_Probabilistic_Embeddings_for_Cross-Modal_Retrieval_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chun_Probabilistic_Embeddings_for_CVPR_2021_supplemental.pdf | null |
Cloud2Curve: Generation and Vectorization of Parametric Sketches | Ayan Das, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song | Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raste... | https://openaccess.thecvf.com/content/CVPR2021/papers/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15536 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Das_Cloud2Curve_Generation_and_Vectorization_of_Parametric_Sketches_CVPR_2021_paper.html | CVPR 2021 | null | null |
TransFill: Reference-Guided Image Inpainting by Merging Multiple Color and Spatial Transformations | Yuqian Zhou, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi | Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image. Most existing technologies exploit patch similarities within the image, or leverage large-scale training data to fill the hole using learned semantic and texture information. However, due t... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.15982 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_TransFill_Reference-Guided_Image_Inpainting_by_Merging_Multiple_Color_and_Spatial_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_TransFill_Reference-Guided_Image_CVPR_2021_supplemental.pdf | null |
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective | Nontawat Charoenphakdee, Jayakorn Vongkulbhisal, Nuttapong Chairatanakul, Masashi Sugiyama | The focal loss has demonstrated its effectiveness in many real-world applications such as object detection and image classification, but its theoretical understanding has been limited so far. In this paper, we first prove that the focal loss is classification-calibrated, i.e., its minimizer surely yields the Bayes-opti... | https://openaccess.thecvf.com/content/CVPR2021/papers/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.pdf | http://arxiv.org/abs/2011.09172 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Charoenphakdee_On_Focal_Loss_for_Class-Posterior_Probability_Estimation_A_Theoretical_Perspective_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Charoenphakdee_On_Focal_Loss_CVPR_2021_supplemental.pdf | null |
VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation | Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen | In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_VIP-DeepLab_Learning_Visual_Perception_With_Depth-Aware_Video_Panoptic_Segmentation_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiao_VIP-DeepLab_Learning_Visual_CVPR_2021_supplemental.pdf | null |
Sequence-to-Sequence Contrastive Learning for Text Recognition | Aviad Aberdam, Ron Litman, Shahar Tsiper, Oron Anschel, Ron Slossberg, Shai Mazor, R. Manmatha, Pietro Perona | We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to c... | https://openaccess.thecvf.com/content/CVPR2021/papers/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.10873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Aberdam_Sequence-to-Sequence_Contrastive_Learning_for_Text_Recognition_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Aberdam_Sequence-to-Sequence_Contrastive_Learning_CVPR_2021_supplemental.pdf | null |
Prototype-Supervised Adversarial Network for Targeted Attack of Deep Hashing | Xunguang Wang, Zheng Zhang, Baoyuan Wu, Fumin Shen, Guangming Lu | Due to its powerful capability of representation learning and high-efficiency computation, deep hashing has made significant progress in large-scale image retrieval. However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval f... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.pdf | http://arxiv.org/abs/2105.07553 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Prototype-Supervised_Adversarial_Network_for_Targeted_Attack_of_Deep_Hashing_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Prototype-Supervised_Adversarial_Network_CVPR_2021_supplemental.pdf | null |
PD-GAN: Probabilistic Diverse GAN for Image Inpainting | Hongyu Liu, Ziyu Wan, Wei Huang, Yibing Song, Xintong Han, Jing Liao | We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and visually realistic content. Our PD-GAN is builtupon a vanilla GAN which generates images based on random noise. During image generation, we mod... | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_PD-GAN_Probabilistic_Diverse_GAN_for_Image_Inpainting_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_PD-GAN_Probabilistic_Diverse_CVPR_2021_supplemental.pdf | null |
Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation | Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph | Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation (e.g., [13, 12]) ... | https://openaccess.thecvf.com/content/CVPR2021/papers/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.07177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Ghiasi_Simple_Copy-Paste_Is_a_Strong_Data_Augmentation_Method_for_Instance_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ghiasi_Simple_Copy-Paste_Is_CVPR_2021_supplemental.pdf | null |
Learning Deep Latent Variable Models by Short-Run MCMC Inference With Optimal Transport Correction | Dongsheng An, Jianwen Xie, Ping Li | Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long run Markov chain Monte Caro (MCMC) or a separate i... | https://openaccess.thecvf.com/content/CVPR2021/papers/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/An_Learning_Deep_Latent_Variable_Models_by_Short-Run_MCMC_Inference_With_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/An_Learning_Deep_Latent_CVPR_2021_supplemental.pdf | null |
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators | Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen | Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. In this work, we investigate the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of r... | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.pdf | http://arxiv.org/abs/2004.14525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Xiong_MobileDets_Searching_for_Object_Detection_Architectures_for_Mobile_Accelerators_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiong_MobileDets_Searching_for_CVPR_2021_supplemental.pdf | null |
Self-Supervised Geometric Perception | Heng Yang, Wei Dong, Luca Carlone, Vladlen Koltun | We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e.g., camera poses, rigid transformations). Our first contribution is to formulate geometric perception as an optimization problem... | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.03114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Self-Supervised_Geometric_Perception_CVPR_2021_supplemental.pdf | null |
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization | Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister | We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then buil... | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.pdf | http://arxiv.org/abs/2104.04015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_CutPaste_Self-Supervised_Learning_CVPR_2021_supplemental.pdf | null |
Open World Compositional Zero-Shot Learning | Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata | Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions some of which might... | https://openaccess.thecvf.com/content/CVPR2021/papers/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mancini_Open_World_Compositional_CVPR_2021_supplemental.pdf | null |
Bi-GCN: Binary Graph Convolutional Network | Junfu Wang, Yunhong Wang, Zhen Yang, Liang Yang, Yuanfang Guo | Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph i... | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Bi-GCN_Binary_Graph_CVPR_2021_supplemental.pdf | null |
Complementary Relation Contrastive Distillation | Jinguo Zhu, Shixiang Tang, Dapeng Chen, Shijie Yu, Yakun Liu, Mingzhe Rong, Aijun Yang, Xiaohua Wang | Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. While we argue that the inter-sample relation conveys abundant information and needs to be distilled... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.pdf | http://arxiv.org/abs/2103.16367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Complementary_Relation_Contrastive_Distillation_CVPR_2021_paper.html | CVPR 2021 | null | null |
UnrealPerson: An Adaptive Pipeline Towards Costless Person Re-Identification | Tianyu Zhang, Lingxi Xie, Longhui Wei, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian | The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part... | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.04268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_UnrealPerson_An_Adaptive_Pipeline_Towards_Costless_Person_Re-Identification_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_UnrealPerson_An_Adaptive_CVPR_2021_supplemental.pdf | null |
Iterative Filter Adaptive Network for Single Image Defocus Deblurring | Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee | We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) that is specifically designed to handle spatially-varying and large defocus blur. For adaptively handling spatially-varying blur, IFAN predic... | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.pdf | https://arxiv.org/abs/2108.13610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Iterative_Filter_Adaptive_Network_for_Single_Image_Defocus_Deblurring_CVPR_2021_paper.html | CVPR 2021 | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Iterative_Filter_Adaptive_CVPR_2021_supplemental.pdf | https://openaccess.thecvf.com |
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning | Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun | We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to... | https://openaccess.thecvf.com/content/CVPR2021/papers/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.pdf | http://arxiv.org/abs/2012.00212 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/html/Luo_UPFlow_Upsampling_Pyramid_for_Unsupervised_Optical_Flow_Learning_CVPR_2021_paper.html | CVPR 2021 | null | null |
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