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title string | authors string | abstract string | pdf string | supp string | arXiv string | bibtex string | url string | detail_url string | tags string | string |
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Vehicle Trajectory Prediction Works, but Not Everywhere | Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi, Mohammad Shaverdikondori, Amir Hossein Shahidzadeh, Seyed-Mohsen Moosavi-Dezfooli, Alexandre Alahi | Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by providing public benchmarks. While state-of-the-art methods are impressive, i.e., they have no off-road prediction, their generalization to citi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bahari_Vehicle_Trajectory_Prediction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bahari_Vehicle_Trajectory_Prediction_Works_but_Not_Everywhere_CVPR_2022_paper.html | CVPR 2022 | null |
PSMNet: Position-Aware Stereo Merging Network for Room Layout Estimation | Haiyan Wang, Will Hutchcroft, Yuguang Li, Zhiqiang Wan, Ivaylo Boyadzhiev, Yingli Tian, Sing Bing Kang | In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360 panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP^2) transformer and a novel Cross-Perspective Pr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_PSMNet_Position-Aware_Stereo_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PSMNet_Position-Aware_Stereo_Merging_Network_for_Room_Layout_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
MonoDTR: Monocular 3D Object Detection With Depth-Aware Transformer | Kuan-Chih Huang, Tsung-Han Wu, Hung-Ting Su, Winston H. Hsu | Monocular 3D object detection is an important yet challenging task in autonomous driving. Some existing methods leverage depth information from an off-the-shelf depth estimator to assist 3D detection, but suffer from the additional computational burden and achieve limited performance caused by inaccurate depth priors. ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_MonoDTR_Monocular_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10981 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_MonoDTR_Monocular_3D_Object_Detection_With_Depth-Aware_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Graph Regularisation for Guided Super-Resolution | Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler | We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potentials make it possible to leverage rich contextual information from the guide image, while the explicit graph optimisation within the architectu... | https://openaccess.thecvf.com/content/CVPR2022/papers/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/de_Lutio_Learning_Graph_Regularisation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/de_Lutio_Learning_Graph_Regularisation_for_Guided_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Instance-Wise Occlusion and Depth Orders in Natural Scenes | Hyunmin Lee, Jaesik Park | In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the spatial relationships of instances in a 3D space. The dataset consists of 2.9M annotations of geometric orderings for class-labeled instances in 101K natural scenes. The scenes were annotated by 3,659 crowd-workers regarding... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Instance-Wise_Occlusion_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Instance-Wise_Occlusion_and_Depth_Orders_in_Natural_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Look for the Change: Learning Object States and State-Modifying Actions From Untrimmed Web Videos | Tomáš Souček, Jean-Baptiste Alayrac, Antoine Miech, Ivan Laptev, Josef Sivic | Human actions often induce changes of object states such as "cutting an apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to temporally localize object states (e.g. "empty" and "full" cup) together with the corresponding state-modifying actions ("pouring coffee") in long uncurated videos with minimal... | https://openaccess.thecvf.com/content/CVPR2022/papers/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Soucek_Look_for_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Soucek_Look_for_the_Change_Learning_Object_States_and_State-Modifying_Actions_CVPR_2022_paper.html | CVPR 2022 | null |
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-Shot Learning | Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, Wenqiang Zhang | This paper presents new hierarchically cascaded transformers that can improve data efficiency through attribute surrogates learning and spectral tokens pooling. Vision transformers have recently been thought of as a promising alternative to convolutional neural networks for visual recognition. But when there is no suff... | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Attribute_Surrogates_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Attribute_Surrogates_Learning_and_Spectral_Tokens_Pooling_in_Transformers_for_CVPR_2022_paper.html | CVPR 2022 | null |
Generalized Category Discovery | Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman | In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. Existing recognition methods are not able to deal with ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Vaze_Generalized_Category_Discovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html | CVPR 2022 | null |
Maximum Consensus by Weighted Influences of Monotone Boolean Functions | Erchuan Zhang, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, Syed Zulqarnain Gilani | Maximisation of Consensus (MaxCon) is one of the most widely used robust criteria in computer vision. Tennakoon et al. (CVPR2021), made a connection between MaxCon and estimation of influences of a Monotone Boolean function. In such, there are two distributions involved: the distribution defining the influence measure;... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Maximum_Consensus_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00953 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Maximum_Consensus_by_Weighted_Influences_of_Monotone_Boolean_Functions_CVPR_2022_paper.html | CVPR 2022 | null |
TransforMatcher: Match-to-Match Attention for Semantic Correspondence | Seungwook Kim, Juhong Min, Minsu Cho | Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic image matching learner, dubbed TransforMatcher, which builds on the success of transformer networks in vis... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_TransforMatcher_Match-to-Match_Attention_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.11634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TransforMatcher_Match-to-Match_Attention_for_Semantic_Correspondence_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Outlier Detection by De-Biasing VAE Likelihoods | Kushal Chauhan, Barath Mohan U, Pradeep Shenoy, Manish Gupta, Devarajan Sridharan | Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chauhan_Robust_Outlier_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.08760 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chauhan_Robust_Outlier_Detection_by_De-Biasing_VAE_Likelihoods_CVPR_2022_paper.html | CVPR 2022 | null |
Contour-Hugging Heatmaps for Landmark Detection | James McCouat, Irina Voiculescu | We propose an effective and easy-to-implement method for simultaneously performing landmark detection in images and obtaining an ingenious uncertainty measurement for each landmark. Uncertainty measurements for landmarks are particularly useful in medical imaging applications: rather than giving an erroneous reading, a... | https://openaccess.thecvf.com/content/CVPR2022/papers/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/McCouat_Contour-Hugging_Heatmaps_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/McCouat_Contour-Hugging_Heatmaps_for_Landmark_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Voxel Field Fusion for 3D Object Detection | Yanwei Li, Xiaojuan Qi, Yukang Chen, Liwei Wang, Zeming Li, Jian Sun, Jiaya Jia | In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cross-modality consistency by representing and fusing augmented image features as a ray in the voxel field. To this end, the learnable sampler i... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.15938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Voxel_Field_Fusion_for_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery | Muli Yang, Yuehua Zhu, Jiaping Yu, Aming Wu, Cheng Deng | In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation. To this end, a model makes use of a base set to learn basic semantic discriminability that can be transferred to ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Divide_and_Conquer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Divide_and_Conquer_Compositional_Experts_for_Generalized_Novel_Class_Discovery_CVPR_2022_paper.html | CVPR 2022 | null |
Programmatic Concept Learning for Human Motion Description and Synthesis | Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu | We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low level motion and high level description as motion concepts. This representation enables human motion description, interactive editing, and controlled synthesis of novel video sequences within a singl... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kulal_Programmatic_Concept_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kulal_Programmatic_Concept_Learning_for_Human_Motion_Description_and_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Interpretable Part-Whole Hierarchies and Conceptual-Semantic Relationships in Neural Networks | Nicola Garau, Niccolò Bisagno, Zeno Sambugaro, Nicola Conci | Deep neural networks achieve outstanding results in a large variety of tasks, often outperforming human experts. However, a known limitation of current neural architectures is the poor accessibility to understand and interpret the network response to a given input. This is directly related to the huge number of variabl... | https://openaccess.thecvf.com/content/CVPR2022/papers/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Garau_Interpretable_Part-Whole_Hierarchies_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03282 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Garau_Interpretable_Part-Whole_Hierarchies_and_Conceptual-Semantic_Relationships_in_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space | Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota | Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling. Recent studies have shown high completion performance with a relatively small window size, but experiments w... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yamamoto_Fast_Algorithm_for_Low-Rank_Tensor_Completion_in_Delay-Embedded_Space_CVPR_2022_paper.html | CVPR 2022 | null |
Panoptic, Instance and Semantic Relations: A Relational Context Encoder To Enhance Panoptic Segmentation | Shubhankar Borse, Hyojin Park, Hong Cai, Debasmit Das, Risheek Garrepalli, Fatih Porikli | This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation. In existing works, it is common to use a shared backbone to extract features for both things (countable classes such as vehicles) and stuff (uncountable classes such as roads). This, however, fails to captu... | https://openaccess.thecvf.com/content/CVPR2022/papers/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Borse_Panoptic_Instance_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Borse_Panoptic_Instance_and_Semantic_Relations_A_Relational_Context_Encoder_To_CVPR_2022_paper.html | CVPR 2022 | null |
Point2Seq: Detecting 3D Objects As Sequences | Yujing Xue, Jiageng Mao, Minzhe Niu, Hang Xu, Michael Bi Mi, Wei Zhang, Xiaogang Wang, Xinchao Wang | We present a simple and effective framework, named Point2Seq, for 3D object detection from point clouds. In contrast to previous methods that normally predict attributes of 3D objects all at once, we expressively model the interdependencies between attributes of 3D objects, which in turn enables a better detection accu... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_Point2Seq_Detecting_3D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13394 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.html | CVPR 2022 | null |
Less Is More: Generating Grounded Navigation Instructions From Landmarks | Su Wang, Ceslee Montgomery, Jordi Orbay, Vighnesh Birodkar, Aleksandra Faust, Izzeddin Gur, Natasha Jaques, Austin Waters, Jason Baldridge, Peter Anderson | We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes. Existing generators suffer from poor visual grounding, causing them to rely on language priors and hallucinate objects. Our MARKY-MT5 system addresses this by focusing on visual landmarks; it comprises a first... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Less_Is_More_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12872 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Less_Is_More_Generating_Grounded_Navigation_Instructions_From_Landmarks_CVPR_2022_paper.html | CVPR 2022 | null |
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition | Shiyuan Huang, Jiawei Ma, Guangxing Han, Shih-Fu Chang | We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown ineffective for FSOR problem due to data limita... | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Task-Adaptive_Negative_Envision_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Task-Adaptive_Negative_Envision_for_Few-Shot_Open-Set_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
DisARM: Displacement Aware Relation Module for 3D Detection | Yao Duan, Chenyang Zhu, Yuqing Lan, Renjiao Yi, Xinwang Liu, Kai Xu | We introduce Displacement Aware Relation Module (DisARM), a novel neural network module for enhancing the performance of 3D object detection in point cloud scenes. The core idea is extracting the most principal contextual information is critical for detection while the target is incomplete or featureless. We find that ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duan_DisARM_Displacement_Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_DisARM_Displacement_Aware_Relation_Module_for_3D_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection | Lingteng Qiu, Zhangyang Xiong, Xuhao Wang, Kenkun Liu, Yihan Li, Guanying Chen, Xiaoguang Han, Shuguang Cui | Waste inspection for packaged waste is an important step in the pipeline of waste disposal. Previous methods either rely on manual visual checking or RGB image-based inspection algorithm, requiring costly preparation procedures (e.g., open the bag and spread the waste items). Moreover, occluded items are very likely to... | https://openaccess.thecvf.com/content/CVPR2022/papers/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qiu_ETHSeg_An_Amodel_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qiu_ETHSeg_An_Amodel_Instance_Segmentation_Network_and_a_Real-World_Dataset_CVPR_2022_paper.html | CVPR 2022 | null |
MixFormer: Mixing Features Across Windows and Dimensions | Qiang Chen, Qiman Wu, Jian Wang, Qinghao Hu, Tao Hu, Errui Ding, Jian Cheng, Jingdong Wang | While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs self-attention within non-overlapped windows and shares weights on the channel dimension. We propose MixFormer to find a solution. First, we... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_MixFormer_Mixing_Features_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02557 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MixFormer_Mixing_Features_Across_Windows_and_Dimensions_CVPR_2022_paper.html | CVPR 2022 | null |
Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC | Xiang An, Jiankang Deng, Jia Guo, Ziyong Feng, XuHan Zhu, Jing Yang, Tongliang Liu | Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets and margin-based softmax loss is the current state-of-the-art approach for face recognition. However, the memory and computing cost of the Fully Connected (FC) layer linearly scales up to the number of identities in the training... | https://openaccess.thecvf.com/content/CVPR2022/papers/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15565 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/An_Killing_Two_Birds_With_One_Stone_Efficient_and_Robust_Training_CVPR_2022_paper.html | CVPR 2022 | null |
NeRF-Editing: Geometry Editing of Neural Radiance Fields | Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, Lin Gao | Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance f... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_NeRF-Editing_Geometry_Editing_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_NeRF-Editing_Geometry_Editing_of_Neural_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Optimal Correction Cost for Object Detection Evaluation | Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh | Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms of the performance of ranked instance retrieval. Such the assumption for the evaluation task does not suit some downstream tasks. To alleviat... | https://openaccess.thecvf.com/content/CVPR2022/papers/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Otani_Optimal_Correction_Cost_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14438 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Otani_Optimal_Correction_Cost_for_Object_Detection_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Contextual Similarity Distillation for Asymmetric Image Retrieval | Hui Wu, Min Wang, Wengang Zhou, Houqiang Li, Qi Tian | Asymmetric image retrieval, which typically uses small model for query side and large model for database server, is an effective solution for resource-constrained scenarios. However, existing approaches either fail to achieve feature coherence or make strong assumptions, e.g., requiring labeled datasets or classifiers ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Contextual_Similarity_Distillation_for_Asymmetric_Image_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
FineDiving: A Fine-Grained Dataset for Procedure-Aware Action Quality Assessment | Jinglin Xu, Yongming Rao, Xumin Yu, Guangyi Chen, Jie Zhou, Jiwen Lu | Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability. We argue that understanding both high-level semantics and internal temporal structures of actions in competiti... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_FineDiving_A_Fine-Grained_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FineDiving_A_Fine-Grained_Dataset_for_Procedure-Aware_Action_Quality_Assessment_CVPR_2022_paper.html | CVPR 2022 | null |
Artistic Style Discovery With Independent Components | Xin Xie, Yi Li, Huaibo Huang, Haiyan Fu, Wanwan Wang, Yanqing Guo | Style transfer has been well studied in recent years with excellent performance processed. While existing methods usually choose CNNs as the powerful tool to accomplish superb stylization, less attention was paid to the latent style space. Rare exploration of underlying dimensions results in the poor style controllabil... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Artistic_Style_Discovery_With_Independent_Components_CVPR_2022_paper.html | CVPR 2022 | null |
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction | Jiacheng Chen, Yiming Qian, Yasutaka Furukawa | This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contri... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_HEAT_Holistic_Edge_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
HyperStyle: StyleGAN Inversion With HyperNetworks for Real Image Editing | Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, Amit Bermano | The inversion of real images into StyleGAN's latent space is a well-studied problem. Nevertheless, applying existing approaches to real-world scenarios remains an open challenge, due to an inherent trade-off between reconstruction and editability: latent space regions which can accurately represent real images typicall... | https://openaccess.thecvf.com/content/CVPR2022/papers/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alaluf_HyperStyle_StyleGAN_Inversion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alaluf_HyperStyle_StyleGAN_Inversion_With_HyperNetworks_for_Real_Image_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
DASO: Distribution-Aware Semantics-Oriented Pseudo-Label for Imbalanced Semi-Supervised Learning | Youngtaek Oh, Dong-Jin Kim, In So Kweon | The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application due to severely biased pseudo-labels caused by (1) class imbalance and (2) class distribution mismatch between labeled and unlabeled data. This paper addresses such a relatively under-explored problem. First, we ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Oh_DASO_Distribution-Aware_Semantics-Oriented_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.05682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Oh_DASO_Distribution-Aware_Semantics-Oriented_Pseudo-Label_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Mobile-Former: Bridging MobileNet and Transformer | Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Xiaoyi Dong, Lu Yuan, Zicheng Liu | We present Mobile-Former, a parallel design of MobileNet and transformer with a two-way bridge in between. This structure leverages the advantages of MobileNet at local processing and transformer at global interaction. And the bridge enables bidirectional fusion of local and global features. Different from recent works... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Mobile-Former_Bridging_MobileNet_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Mobile-Former_Bridging_MobileNet_and_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation | Andra Petrovai, Sergiu Nedevschi | We present a novel self-distillation based self-supervised monocular depth estimation (SD-SSMDE) learning framework. In the first step, our network is trained in a self-supervised regime on high-resolution images with the photometric loss. The network is further used to generate pseudo depth labels for all the images i... | https://openaccess.thecvf.com/content/CVPR2022/papers/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Petrovai_Exploiting_Pseudo_Labels_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Petrovai_Exploiting_Pseudo_Labels_in_a_Self-Supervised_Learning_Framework_for_Improved_CVPR_2022_paper.html | CVPR 2022 | null |
DESTR: Object Detection With Split Transformer | Liqiang He, Sinisa Todorovic | Self- and cross-attention in Transformers provide for high model capacity, making them viable models for object detection. However, Transformers still lag in performance behind CNN-based detectors. This is, we believe, because: (a) Cross-attention is used for both classification and bounding-box regression tasks; (b) T... | https://openaccess.thecvf.com/content/CVPR2022/papers/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_DESTR_Object_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_DESTR_Object_Detection_With_Split_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
LTP: Lane-Based Trajectory Prediction for Autonomous Driving | Jingke Wang, Tengju Ye, Ziqing Gu, Junbo Chen | The reasonable trajectory prediction of surrounding traffic participants is crucial for autonomous driving. Especially, how to predict multiple plausible trajectories is still a challenging problem because of the multiple possibilities of the future. Proposal-based prediction methods address the multi-modality issues w... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_LTP_Lane-Based_Trajectory_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_LTP_Lane-Based_Trajectory_Prediction_for_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
CycleMix: A Holistic Strategy for Medical Image Segmentation From Scribble Supervision | Ke Zhang, Xiahai Zhuang | Curating a large set of fully annotated training data can be costly, especially for the tasks of medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in practice, but training segmentation models from limited supervision of scribbles is still challenging. To address the difficulties, we... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_CycleMix_A_Holistic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CycleMix_A_Holistic_Strategy_for_Medical_Image_Segmentation_From_Scribble_CVPR_2022_paper.html | CVPR 2022 | null |
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution | Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, Xiaolong Wang | Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_VideoINR_Learning_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Towards End-to-End Unified Scene Text Detection and Layout Analysis | Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis | Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel resear... | https://openaccess.thecvf.com/content/CVPR2022/papers/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Long_Towards_End-to-End_Unified_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Towards_End-to-End_Unified_Scene_Text_Detection_and_Layout_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Image Based Reconstruction of Liquids From 2D Surface Detections | Florian Richter, Ryan K. Orosco, Michael C. Yip | In this work, we present a solution to the challenging problem of reconstructing liquids from image data. The challenges in reconstructing liquids, which is not faced in previous reconstruction works on rigid and deforming surfaces, lies in the inability to use depth sensing and color features due the variable index of... | https://openaccess.thecvf.com/content/CVPR2022/papers/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Richter_Image_Based_Reconstruction_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.11491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Richter_Image_Based_Reconstruction_of_Liquids_From_2D_Surface_Detections_CVPR_2022_paper.html | CVPR 2022 | null |
Contextual Outpainting With Object-Level Contrastive Learning | Jiacheng Li, Chang Chen, Zhiwei Xiong | We study the problem of contextual outpainting, which aims to hallucinate the missing background contents based on the remaining foreground contents. Existing image outpainting methods focus on completing object shapes or extending existing scenery textures, neglecting the semantically meaningful relationship between t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Contextual_Outpainting_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Contextual_Outpainting_With_Object-Level_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network | Wooseok Lee, Sanghyun Son, Kyoung Mu Lee | Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical assumptions like pixel-wise independent noise. Hence, it is challenging to deal with spatially correlated real-world noise using self-... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_AP-BSN_Self-Supervised_Denoising_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_AP-BSN_Self-Supervised_Denoising_for_Real-World_Images_via_Asymmetric_PD_and_CVPR_2022_paper.html | CVPR 2022 | null |
AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation | Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, Shubham Tulsiani | Powerful priors allow us to perform inference with insufficient information. In this paper, we propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape completion, reconstruction, and generation. We model the distribution over 3D shapes as a non-sequential autoregressive distribution ove... | https://openaccess.thecvf.com/content/CVPR2022/papers/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09516 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_AutoSDF_Shape_Priors_for_3D_Completion_Reconstruction_and_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior | Metin Ersin Arican, Ozgur Kara, Gustav Bredell, Ender Konukoglu | Recent works show that convolutional neural network (CNN) architectures have a spectral bias towards lower frequencies, which has been leveraged for various image restoration tasks in the Deep Image Prior (DIP) framework. The benefit of the inductive bias the network imposes in the DIP framework depends on the architec... | https://openaccess.thecvf.com/content/CVPR2022/papers/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Arican_ISNAS-DIP_Image-Specific_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Arican_ISNAS-DIP_Image-Specific_Neural_Architecture_Search_for_Deep_Image_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
Depth-Guided Sparse Structure-From-Motion for Movies and TV Shows | Sheng Liu, Xiaohan Nie, Raffay Hamid | Existing approaches for Structure from Motion (SfM) produce impressive 3D reconstruction results especially when using imagery captured with large parallax. However, to create engaging video-content in movies and TV shows, the amount by which a camera can be moved while filming a particular shot is often limited. The r... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Depth-Guided_Sparse_Structure-From-Motion_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Depth-Guided_Sparse_Structure-From-Motion_for_Movies_and_TV_Shows_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Referring Video Object Segmentation With Multimodal Transformers | Adam Botach, Evgenii Zheltonozhskii, Chaim Baskin | The referring video object segmentation task (RVOS) involves segmentation of a text-referred object instance in the frames of a given video. Due to the complex nature of this multimodal task, which combines text reasoning, video understanding, instance segmentation and tracking, existing approaches typically rely on so... | https://openaccess.thecvf.com/content/CVPR2022/papers/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Botach_End-to-End_Referring_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14821 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Botach_End-to-End_Referring_Video_Object_Segmentation_With_Multimodal_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping | Yifang Men, Yuan Yao, Miaomiao Cui, Zhouhui Lian, Xuansong Xie, Xian-Sheng Hua | In this paper, we present a general-purpose solution to cartoon image synthesis with unpaired training data. In contrast to previous works learning pre-defined cartoon styles for specified usage scenarios (portrait or scene), we aim to train a common cartoon translator which can not only simultaneously render exaggerat... | https://openaccess.thecvf.com/content/CVPR2022/papers/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Men_Unpaired_Cartoon_Image_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Men_Unpaired_Cartoon_Image_Synthesis_via_Gated_Cycle_Mapping_CVPR_2022_paper.html | CVPR 2022 | null |
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo | Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys | We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_IterMVS_Iterative_Probability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_IterMVS_Iterative_Probability_Estimation_for_Efficient_Multi-View_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Not All Points Are Equal: Learning Highly Efficient Point-Based Detectors for 3D LiDAR Point Clouds | Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo | We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Not_All_Points_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Not_All_Points_Are_Equal_Learning_Highly_Efficient_Point-Based_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Jingyi Xu, Zihan Chen, Tony Q.S. Quek, Kai Fong Ernest Chong | Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized lear... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_FedCorr_Multi-Stage_Federated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html | CVPR 2022 | null |
Detecting Camouflaged Object in Frequency Domain | Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, Shouhong Ding | Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in their environment, which has various downstream applications in fields such as medicine, art, and agriculture. However, it is an extremely challenging task to spot camouflaged objects with the perception ability of human eyes. He... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_Detecting_Camouflaged_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Detecting_Camouflaged_Object_in_Frequency_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
RigNeRF: Fully Controllable Neural 3D Portraits | ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu | Volumetric neural rendering methods, such as neural ra-diance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head,within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Athar_RigNeRF_Fully_Controllable_Neural_3D_Portraits_CVPR_2022_paper.html | CVPR 2022 | null |
CLIP-Forge: Towards Zero-Shot Text-To-Shape Generation | Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan | Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale. We pr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sanghi_CLIP-Forge_Towards_Zero-Shot_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sanghi_CLIP-Forge_Towards_Zero-Shot_Text-To-Shape_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Style-Based Global Appearance Flow for Virtual Try-On | Sen He, Yi-Zhe Song, Tao Xiang | Image-based virtual try-on aims to fit an in-shop garment into a clothed person image. To achieve this, a key step is garment warping which spatially aligns the target garment with the corresponding body parts in the person image. Prior methods typically adopt a local appearance flow estimation model. They are thus int... | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.01046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Style-Based_Global_Appearance_Flow_for_Virtual_Try-On_CVPR_2022_paper.html | CVPR 2022 | null |
Source-Free Object Detection by Learning To Overlook Domain Style | Shuaifeng Li, Mao Ye, Xiatian Zhu, Lihua Zhou, Lin Xiong | Source-free object detection (SFOD) needs to adapt a detector pre-trained on a labeled source domain to a target domain, with only unlabeled training data from the target domain. Existing SFOD methods typically adopt the pseudo labeling paradigm with model adaption alternating between predicting pseudo labels and fine-... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Source-Free_Object_Detection_by_Learning_To_Overlook_Domain_Style_CVPR_2022_paper.html | CVPR 2022 | null |
Active Learning for Open-Set Annotation | Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang | Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation tasks, the unlabeled data usually contains a large amount of examples from unknown classes, resulting in the failure of most active learnin... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.06758 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ning_Active_Learning_for_Open-Set_Annotation_CVPR_2022_paper.html | CVPR 2022 | null |
SceneSqueezer: Learning To Compress Scene for Camera Relocalization | Luwei Yang, Rakesh Shrestha, Wenbo Li, Shuaicheng Liu, Guofeng Zhang, Zhaopeng Cui, Ping Tan | Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image. Storing these pre-built 3D scene models can be prohibitively expensive for large-scale environments, especially on mobile devices with limited storage and communi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_SceneSqueezer_Learning_To_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | CVPR 2022 | null |
SelfRecon: Self Reconstruction Your Digital Avatar From Monocular Video | Boyi Jiang, Yang Hong, Hujun Bao, Juyong Zhang | We propose SelfRecon, a clothed human body reconstruction method that combines implicit and explicit representations to recover space-time coherent geometries from a monocular self-rotating human video. Explicit methods require a predefined template mesh for a given sequence, while the template is hard to acquire for a... | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.12792 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_SelfRecon_Self_Reconstruction_Your_Digital_Avatar_From_Monocular_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Instance-Dependent Label-Noise Learning With Manifold-Regularized Transition Matrix Estimation | De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, Masashi Sugiyama | In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers. However, it is very challenging to estimate the transition matrix T(x), where T(x) denotes the instance, because it is unidentifiable un... | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Instance-Dependent_Label-Noise_Learning_With_Manifold-Regularized_Transition_Matrix_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance With Expanded Views | Junbo Zhang, Kaisheng Ma | A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the pre-determined composition of multiple data augmentations brings two drawbacks. First, the artificial choice of augmentation types brings speci... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2206.00227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Rethinking_the_Augmentation_Module_in_Contrastive_Learning_Learning_Hierarchical_Augmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Models Are Continual Learners | Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal | Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically reduced in a Continual Learning (CL) scenario where data is presented to the model sequentially. I... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fini_Self-Supervised_Models_Are_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fini_Self-Supervised_Models_Are_Continual_Learners_CVPR_2022_paper.html | CVPR 2022 | null |
Dreaming To Prune Image Deraining Networks | Weiqi Zou, Yang Wang, Xueyang Fu, Yang Cao | Convolutional image deraining networks have achieved great success while suffering from tremendous computational and memory costs. Most model compression methods require original data for iterative fine-tuning, which is limited in real-world applications due to storage, privacy, and transmission constraints. We note th... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zou_Dreaming_To_Prune_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_Dreaming_To_Prune_Image_Deraining_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing | Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma | Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness --- some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacrifice flexible trans... | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_Equivariant_Point_Cloud_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14486 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Equivariant_Point_Cloud_Analysis_via_Learning_Orientations_for_Message_Passing_CVPR_2022_paper.html | CVPR 2022 | null |
When Does Contrastive Visual Representation Learning Work? | Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie | Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this su... | https://openaccess.thecvf.com/content/CVPR2022/papers/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2105.05837 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cole_When_Does_Contrastive_Visual_Representation_Learning_Work_CVPR_2022_paper.html | CVPR 2022 | null |
One Step at a Time: Long-Horizon Vision-and-Language Navigation With Milestones | Chan Hee Song, Jihyung Kil, Tai-Yu Pan, Brian M. Sadler, Wei-Lun Chao, Yu Su | We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task. Significant progress has been made in recent years, especially for tasks with short horizons. However, when it comes to long-horizon tasks with extended seq... | https://openaccess.thecvf.com/content/CVPR2022/papers/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.07028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Song_One_Step_at_a_Time_Long-Horizon_Vision-and-Language_Navigation_With_Milestones_CVPR_2022_paper.html | CVPR 2022 | null |
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization | Wei Dong, Junsheng Wu, Yi Luo, Zongyuan Ge, Peng Wang | The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations o... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Node_Representation_Learning_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.12265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Node_Representation_Learning_in_Graph_via_Node-to-Neighbourhood_Mutual_Information_Maximization_CVPR_2022_paper.html | CVPR 2022 | null |
Point Cloud Pre-Training With Natural 3D Structures | Ryosuke Yamada, Hirokatsu Kataoka, Naoya Chiba, Yukiyasu Domae, Tetsuya Ogata | The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-driven supervised lear... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yamada_Point_Cloud_Pre-Training_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.html | CVPR 2022 | null |
Scene Consistency Representation Learning for Video Scene Segmentation | Haoqian Wu, Keyu Chen, Yanan Luo, Ruizhi Qiao, Bo Ren, Haozhe Liu, Weicheng Xie, Linlin Shen | A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a sce... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Scene_Consistency_Representation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.05487 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart | Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu | Correctly classifying adversarial examples is an essential but challenging requirement for safely deploying machine learning models. As reported in RobustBench, even the state-of-the-art adversarially trained models struggle to exceed 67% robust test accuracy on CIFAR-10, which is far from practical. A complementary wa... | https://openaccess.thecvf.com/content/CVPR2022/papers/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pang_Two_Coupled_Rejection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.14785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Two_Coupled_Rejection_Metrics_Can_Tell_Adversarial_Examples_Apart_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Explainable Metrics for Augmented SGD | Mahdi S. Hosseini, Mathieu Tuli, Konstantinos N. Plataniotis | Explaining the generalization characteristics of deep learning is an emerging topic in advanced machine learning. There are several unanswered questions about how learning under stochastic optimization really works and why certain strategies are better than others. In this paper, we address the following question: can ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hosseini_Exploiting_Explainable_Metrics_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16723 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hosseini_Exploiting_Explainable_Metrics_for_Augmented_SGD_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Video Semantic Segmentation With Inter-Frame Feature Reconstruction | Jiafan Zhuang, Zilei Wang, Yuan Gao | One major challenge for semantic segmentation in real-world scenarios is only limited pixel-level labels available due to high expense of human labor though a vast volume of video data is provided. Existing semi-supervised methods attempt to exploit unlabeled data in model training, but they just regard video as a set ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhuang_Semi-Supervised_Video_Semantic_Segmentation_With_Inter-Frame_Feature_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
GenDR: A Generalized Differentiable Renderer | Felix Petersen, Bastian Goldluecke, Christian Borgelt, Oliver Deussen | In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component.We instantiate our general differentiable renderer, which generalizes existing differentiable rendere... | https://openaccess.thecvf.com/content/CVPR2022/papers/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Petersen_GenDR_A_Generalized_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Petersen_GenDR_A_Generalized_Differentiable_Renderer_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Neural Implicit Surfaces Geometry With Patch Warping | François Darmon, Bénédicte Bascle, Jean-Clément Devaux, Pascal Monasse, Mathieu Aubry | Neural implicit surfaces have become an important technique for multi-view 3D reconstruction but their accuracy remains limited. In this paper, we argue that this comes from the difficulty to learn and render high frequency textures with neural networks. We thus propose to add to the standard neural rendering optimizat... | https://openaccess.thecvf.com/content/CVPR2022/papers/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Darmon_Improving_Neural_Implicit_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09648 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Darmon_Improving_Neural_Implicit_Surfaces_Geometry_With_Patch_Warping_CVPR_2022_paper.html | CVPR 2022 | null |
XYLayoutLM: Towards Layout-Aware Multimodal Networks for Visually-Rich Document Understanding | Zhangxuan Gu, Changhua Meng, Ke Wang, Jun Lan, Weiqiang Wang, Ming Gu, Liqing Zhang | Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings. However, most existing approaches utilize the position embeddings to incorporate the sequence information,... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_XYLayoutLM_Towards_Layout-Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06947 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_XYLayoutLM_Towards_Layout-Aware_Multimodal_Networks_for_Visually-Rich_Document_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Amodal Segmentation Through Out-of-Task and Out-of-Distribution Generalization With a Bayesian Model | Yihong Sun, Adam Kortylewski, Alan Yuille | Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Ther... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Amodal_Segmentation_Through_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.13175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | CVPR 2022 | null |
How Well Do Sparse ImageNet Models Transfer? | Eugenia Iofinova, Alexandra Peste, Mark Kurtz, Dan Alistarh | Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "downstream". In this work, we perform an in-depth in... | https://openaccess.thecvf.com/content/CVPR2022/papers/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Iofinova_How_Well_Do_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13445 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Iofinova_How_Well_Do_Sparse_ImageNet_Models_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
REX: Reasoning-Aware and Grounded Explanation | Shi Chen, Qi Zhao | Effectiveness and interpretability are two essential properties for trustworthy AI systems. Most recent studies in visual reasoning are dedicated to improving the accuracy of predicted answers, and less attention is paid to explaining the rationales behind the decisions. As a result, they commonly take advantage of spu... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_REX_Reasoning-Aware_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06107 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_REX_Reasoning-Aware_and_Grounded_Explanation_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic Dual-Output Diffusion Models | Yaniv Benny, Lior Wolf | Iterative denoising-based generation, also known as denoising diffusion models, has recently been shown to be comparable in quality to other classes of generative models, and even surpass them. Including, in particular, Generative Adversarial Networks, which are currently the state of the art in many sub-tasks of image... | https://openaccess.thecvf.com/content/CVPR2022/papers/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Benny_Dynamic_Dual-Output_Diffusion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Benny_Dynamic_Dual-Output_Diffusion_Models_CVPR_2022_paper.html | CVPR 2022 | null |
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis | Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu | Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for robustness and fairness, e.g., inability to synthesize the face images of underrepr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_StyleT2I_Toward_Compositional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_StyleT2I_Toward_Compositional_and_High-Fidelity_Text-to-Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
JoinABLe: Learning Bottom-Up Assembly of Parametric CAD Joints | Karl D.D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik | Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by aligning individual parts to one another using constraints called joints. In this paper we introduce JoinABLe, a learning-based method that assembl... | https://openaccess.thecvf.com/content/CVPR2022/papers/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Willis_JoinABLe_Learning_Bottom-Up_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Willis_JoinABLe_Learning_Bottom-Up_Assembly_of_Parametric_CAD_Joints_CVPR_2022_paper.html | CVPR 2022 | null |
CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphism | Jiahui Lei, Kostas Daniilidis | While neural representations for static 3D shapes are widely studied, representations for deformable surfaces are limited to be template-dependent or to lack efficiency. We introduce Canonical Deformation Coordinate Space (CaDeX), a unified representation of both shape and nonrigid motion. Our key insight is the factor... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lei_CaDeX_Learning_Canonical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_CaDeX_Learning_Canonical_Deformation_Coordinate_Space_for_Dynamic_Surface_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes | Yang You, Zelin Ye, Yujing Lou, Chengkun Li, Yong-Lu Li, Lizhuang Ma, Weiming Wang, Cewu Lu | 3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network. Both their offset and orientat... | https://openaccess.thecvf.com/content/CVPR2022/papers/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/You_Canonical_Voting_Towards_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.12001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/You_Canonical_Voting_Towards_Robust_Oriented_Bounding_Box_Detection_in_3D_CVPR_2022_paper.html | CVPR 2022 | null |
V-Doc: Visual Questions Answers With Documents | Yihao Ding, Zhe Huang, Runlin Wang, YanHang Zhang, Xianru Chen, Yuzhong Ma, Hyunsuk Chung, Soyeon Caren Han | We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_V-Doc_Visual_Questions_Answers_With_Documents_CVPR_2022_paper.html | CVPR 2022 | null |
AEGNN: Asynchronous Event-Based Graph Neural Networks | Simon Schaefer, Daniel Gehrig, Davide Scaramuzza | The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity and high temporal resolution of events, leading to high computational burden and latency. For this reas... | https://openaccess.thecvf.com/content/CVPR2022/papers/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Schaefer_AEGNN_Asynchronous_Event-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Schaefer_AEGNN_Asynchronous_Event-Based_Graph_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Layer-Wised Model Aggregation for Personalized Federated Learning | Xiaosong Ma, Jie Zhang, Song Guo, Wenchao Xu | Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Researches over the past few years have applied the weighted aggregation manner to produce personalized models, where the weights are determine... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Layer-Wised_Model_Aggregation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values | Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk | We present Polarity Sampling, a theoretically justified plug-and-play method for controlling the generation quality and diversity of any pre-trained deep generative network (DGN). Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space... | https://openaccess.thecvf.com/content/CVPR2022/papers/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Humayun_Polarity_Sampling_Quality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01993 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Humayun_Polarity_Sampling_Quality_and_Diversity_Control_of_Pre-Trained_Generative_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization | Yuan-kui Li, Yun-Hsuan Lien, Yu-Shuen Wang | In this study, we present a colorization network that generates flat-color icons according to given sketches and semantic colorization styles. Specifically, our network contains a style-structure disentangled colorization module and a normalizing flow. The colorization module transforms a paired sketch image and style ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Style-Structure_Disentangled_Features_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Style-Structure_Disentangled_Features_and_Normalizing_Flows_for_Diverse_Icon_Colorization_CVPR_2022_paper.html | CVPR 2022 | null |
Object-Aware Video-Language Pre-Training for Retrieval | Jinpeng Wang, Yixiao Ge, Guanyu Cai, Rui Yan, Xudong Lin, Ying Shan, Xiaohu Qie, Mike Zheng Shou | Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.00656 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Object-Aware_Video-Language_Pre-Training_for_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
OSKDet: Orientation-Sensitive Keypoint Localization for Rotated Object Detection | Dongchen Lu, Dongmei Li, Yali Li, Shengjin Wang | Rotated object detection is a challenging issue in computer vision field. Inadequate rotated representation and the confusion of parametric regression have been the bottleneck for high performance rotated detection. In this paper, we propose an orientation-sensitive keypoint based rotated detector OSKDet. First, we ado... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_OSKDet_Orientation-Sensitive_Keypoint_Localization_for_Rotated_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
MAT: Mask-Aware Transformer for Large Hole Image Inpainting | Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, Jiaya Jia | Recent studies have shown the importance of modeling long-range interactions in the inpainting problem. To achieve this goal, existing approaches exploit either standalone attention techniques or transformers, but usually under a low resolution in consideration of computational cost. In this paper, we present a novel t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_MAT_Mask-Aware_Transformer_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15270 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_MAT_Mask-Aware_Transformer_for_Large_Hole_Image_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Geometric Consistency for Monocular 3D Object Detection | Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang | This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of ge... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lian_Exploring_Geometric_Consistency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.05858 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lian_Exploring_Geometric_Consistency_for_Monocular_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Window Fully-Connected CRFs for Monocular Depth Estimation | Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, Ping Tan | Estimating the accurate depth from a single image is challenging since it is inherently ambiguous and ill-posed. While recent works design increasingly complicated and powerful networks to directly regress the depth map, we take the path of CRFs optimization. Due to the expensive computation, CRFs are usually performed... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.01502 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Neural_Window_Fully-Connected_CRFs_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
CodedVTR: Codebook-Based Sparse Voxel Transformer With Geometric Guidance | Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang | Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on massive-scale pre-training and sophisticated training techniques. When applying to 3D tasks, the irregular data structure and limited data s... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_CodedVTR_Codebook-Based_Sparse_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_CodedVTR_Codebook-Based_Sparse_Voxel_Transformer_With_Geometric_Guidance_CVPR_2022_paper.html | CVPR 2022 | null |
Uncertainty-Aware Deep Multi-View Photometric Stereo | Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool | This paper presents a simple and effective solution to the longstanding classical multi-view photometric stereo (MVPS) problem. It is well-known that photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kaya_Uncertainty-Aware_Deep_Multi-View_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2202.13071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kaya_Uncertainty-Aware_Deep_Multi-View_Photometric_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Coherent Point Drift Revisited for Non-Rigid Shape Matching and Registration | Aoxiang Fan, Jiayi Ma, Xin Tian, Xiaoguang Mei, Wei Liu | In this paper, we explore a new type of extrinsic method to directly align two geometric shapes with point-to-point correspondences in ambient space by recovering a deformation, which allows more continuous and smooth maps to be obtained. Specifically, the classic coherent point drift is revisited and generalizations h... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Coherent_Point_Drift_Revisited_for_Non-Rigid_Shape_Matching_and_Registration_CVPR_2022_paper.html | CVPR 2022 | null |
Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification | Zizheng Yang, Xin Jin, Kecheng Zheng, Feng Zhao | Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID is more challenging and exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised represent... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Unleashing_Potential_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unleashing_Potential_of_Unsupervised_Pre-Training_With_Intra-Identity_Regularization_for_Person_CVPR_2022_paper.html | CVPR 2022 | null |
Align and Prompt: Video-and-Language Pre-Training With Entity Prompts | Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H. Hoi | Video-and-language pre-training has shown promising improvements on various downstream tasks. Most previous methods capture cross-modal interactions with a transformer-based multimodal encoder, not fully addressing the misalignment between unimodal video and text features. Besides, learning fine-grained visual-language... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.09583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Align_and_Prompt_Video-and-Language_Pre-Training_With_Entity_Prompts_CVPR_2022_paper.html | CVPR 2022 | null |
A Unified Query-Based Paradigm for Point Cloud Understanding | Zetong Yang, Li Jiang, Yanan Sun, Bernt Schiele, Jiaya Jia | 3D point cloud understanding is an important component in autonomous driving and robotics. In this paper, we present a novel Embedding-Querying paradigm (EQ- Paradigm) for 3D understanding tasks including detection, segmentation and classification. EQ-Paradigm is a unified paradigm that enables combination of existing ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_A_Unified_Query-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01252 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_A_Unified_Query-Based_Paradigm_for_Point_Cloud_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
It's About Time: Analog Clock Reading in the Wild | Charig Yang, Weidi Xie, Andrew Zisserman | In this paper, we present a framework for reading analog clocks in natural images or videos. Specifically, we make the following contributions: First, we create a scalable pipeline for generating synthetic clocks, significantly reducing the requirements for the labour-intensive annotations; Second, we introduce a clock... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Its_About_Time_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Its_About_Time_Analog_Clock_Reading_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
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