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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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Knowledge Mining With Scene Text for Fine-Grained Recognition | Hao Wang, Junchao Liao, Tianheng Cheng, Zewen Gao, Hao Liu, Bo Ren, Xiang Bai, Wenyu Liu | Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14215 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Learning of Object Parts for Semantic Segmentation | Adrian Ziegler, Yuki M. Asano | Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation have not benefited from this trend as they require spatially-diverse representations. However, learning... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ziegler_Self-Supervised_Learning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects | Manuel Stoiber, Martin Sundermeyer, Rudolph Triebel | Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem. However, while they achieve impressive results, many objects do not contain sufficient texture, violating the main underlying assumption. ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Stoiber_Iterative_Corresponding_Geometry_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.html | CVPR 2022 | null |
Single-Photon Structured Light | Varun Sundar, Sizhuo Ma, Aswin C. Sankaranarayanan, Mohit Gupta | We present a novel structured light technique that uses Single Photon Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and low-light levels. This technique, called "Single-Photon Structured Light", works by sensing binary images that indicates the presence or absence of photon arrivals during eac... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sundar_Single-Photon_Structured_Light_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.html | CVPR 2022 | null |
Deblurring via Stochastic Refinement | Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, Peyman Milanfar | Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead... | https://openaccess.thecvf.com/content/CVPR2022/papers/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Whang_Deblurring_via_Stochastic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html | CVPR 2022 | null |
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds | Daigang Cai, Lichen Zhao, Jing Zhang, Lu Sheng, Dong Xu | Observing that the 3D captioning task and the 3D grounding task contain both shared and complementary information in nature, in this work, we propose a unified framework to jointly solve these two distinct but closely related tasks in a synergistic fashion, which consists of both shared task-agnostic modules and lightw... | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.html | CVPR 2022 | null |
TransGeo: Transformer Is All You Need for Cross-View Image Geo-Localization | Sijie Zhu, Mubarak Shah, Chen Chen | The dominant CNN-based methods for cross-view image geo-localization rely on polar transform and fail to model global correlation. We propose a pure transformer-based approach (TransGeo) to address these limitations from a different perspective. TransGeo takes full advantage of the strengths of transformer related to g... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_TransGeo_Transformer_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00097 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.html | CVPR 2022 | null |
R(Det)2: Randomized Decision Routing for Object Detection | Yali Li, Shengjin Wang | In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end l... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RDet2_Randomized_Decision_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Abandoning the Bayer-Filter To See in the Dark | Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, Jiajun Shen | Low-light image enhancement, a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present a De-Bayer-Filter s... | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Abandoning_the_Bayer-Filter_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html | CVPR 2022 | null |
SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention | Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig | We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this informat... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wodlinger_SASIC_Stereo_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Exploiting Temporal Relations on Radar Perception for Autonomous Driving | Peizhao Li, Pu Wang, Karl Berntorp, Hongfu Liu | We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surroundi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Exploiting_Temporal_Relations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Instance Point Cloud Registration by Efficient Correspondence Clustering | Weixuan Tang, Danping Zou | We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outli... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Multi-Instance_Point_Cloud_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.14582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Boundary Learning for Point Cloud Segmentation | Liyao Tang, Yibing Zhan, Zhe Chen, Baosheng Yu, Dacheng Tao | Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explor... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Contrastive_Boundary_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution | Jie Liang, Hui Zeng, Lei Zhang | Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this p... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Details_or_Artifacts_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.html | CVPR 2022 | null |
CVNet: Contour Vibration Network for Building Extraction | Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang | The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibration network (CVNet) for automatic building boundary delineation. Different from the previous contour mo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.html | CVPR 2022 | null |
Hyperbolic Image Segmentation | Mina Ghadimi Atigh, Julian Schoep, Erman Acar, Nanne van Noord, Pascal Mettes | For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pix... | https://openaccess.thecvf.com/content/CVPR2022/papers/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Forward Compatible Training for Large-Scale Embedding Retrieval Systems | Vivek Ramanujan, Pavan Kumar Anasosalu Vasu, Ali Farhadi, Oncel Tuzel, Hadi Pouransari | In visual retrieval systems, updating the embedding model requires recomputing features for every piece of data. This expensive process is referred to as backfilling. Recently, the idea of backward compatible training (BCT) was proposed. To avoid the cost of backfilling, BCT modifies training of the new model to make i... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramanujan_Forward_Compatible_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02805 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.html | CVPR 2022 | null |
Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval | Nina Shvetsova, Brian Chen, Andrew Rouditchenko, Samuel Thomas, Brian Kingsbury, Rogerio S. Feris, David Harwath, James Glass, Hilde Kuehne | Multi-modal learning from video data has seen increased attention recently as it allows training of semantically meaningful embeddings without human annotation, enabling tasks like zero-shot retrieval and action localization. In this work, we present a multi-modal, modality agnostic fusion transformer that learns to ex... | https://openaccess.thecvf.com/content/CVPR2022/papers/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shvetsova_Everything_at_Once_CVPR_2022_supplemental.pdf | https://arxiv.org/abs/2112.04446 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
Swin Transformer V2: Scaling Up Capacity and Resolution | Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo | We present techniques for scaling Swin Transformer [??] up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution. By scaling up capacity and resolution, Swin Transformer sets new records on four representative vision benchmarks: 84.0% top-1 accuracy on ImageNet-V2 image c... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Swin_Transformer_V2_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09883 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes | Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu | This paper introduces a novel framework called DT-Net for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insigh... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hui_Neural_Template_Topology-Aware_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.html | CVPR 2022 | null |
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints | Zhendong Zhao, Xiaojun Chen, Yuexin Xuan, Ye Dong, Dakui Wang, Kaitai Liang | Backdoor attack is a type of serious security threat to deep learning models.An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.html | CVPR 2022 | null |
Projective Manifold Gradient Layer for Deep Rotation Regression | Jiayi Chen, Yingda Yin, Tolga Birdal, Baoquan Chen, Leonidas J. Guibas, He Wang | Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem. The gap between the Euclidean network output space and the non-Euclidean SO(3) manifold imposes a severe challenge for neural network learning in both forward and backward passes. While several works have proposed di... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Projective_Manifold_Gradient_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.11657 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation | Jinheng Xie, Xianxu Hou, Kai Ye, Linlin Shen | It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_CLIMS_Cross_Language_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Refactor Action and Co-Occurrence Features for Temporal Action Localization | Kun Xia, Le Wang, Sanping Zhou, Nanning Zheng, Wei Tang | The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurri... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Learning_To_Refactor_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | CVPR 2022 | null |
It's Time for Artistic Correspondence in Music and Video | Dídac Surís, Carl Vondrick, Bryan Russell, Justin Salamon | We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capt... | https://openaccess.thecvf.com/content/CVPR2022/papers/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suris_Its_Time_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Mixed Differential Privacy in Computer Vision | Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto | We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yie... | https://openaccess.thecvf.com/content/CVPR2022/papers/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Golatkar_Mixed_Differential_Privacy_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.html | CVPR 2022 | null |
AdaFace: Quality Adaptive Margin for Face Recognition | Minchul Kim, Anil K. Jain, Xiaoming Liu | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_AdaFace_Quality_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss | Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, Yansheng Li | Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_Learning_Soft_Estimator_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.html | CVPR 2022 | null |
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising | Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Lei Zhang | We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimiza... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_DN-DETR_Accelerate_DETR_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.html | CVPR 2022 | null |
HCSC: Hierarchical Contrastive Selective Coding | Yuanfan Guo, Minghao Xu, Jiawen Li, Bingbing Ni, Xuanyu Zhu, Zhenbang Sun, Yi Xu | Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image representations can greatly benefit the semantic understanding on various downs... | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_HCSC_Hierarchical_Contrastive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.00455 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.html | CVPR 2022 | null |
TransRank: Self-Supervised Video Representation Learning via Ranking-Based Transformation Recognition | Haodong Duan, Nanxuan Zhao, Kai Chen, Dahua Lin | Recognizing transformation types applied to a video clip (RecogTrans) is a long-established paradigm for self-supervised video representation learning, which achieves much inferior performance compared to instance discrimination approaches (InstDisc) in recent works. However, based on a thorough comparison of represent... | https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duan_TransRank_Self-Supervised_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos | David Novotny, Ignacio Rocco, Samarth Sinha, Alexandre Carlier, Gael Kerchenbaum, Roman Shapovalov, Nikita Smetanin, Natalia Neverova, Benjamin Graham, Andrea Vedaldi | We consider the problem of reconstructing the depth of dynamic objects from videos. Recent progress in dynamic video depth prediction has focused on improving the output of monocular depth estimators by means of multi-view constraints while imposing little to no restrictions on the deformation of the dynamic parts of t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.html | CVPR 2022 | null |
Invariant Grounding for Video Question Answering | Yicong Li, Xiang Wang, Junbin Xiao, Wei Ji, Tat-Seng Chua | Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Invariant_Grounding_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Prompt Distribution Learning | Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian | We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Prompt_Distribution_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
RAGO: Recurrent Graph Optimizer for Multiple Rotation Averaging | Heng Li, Zhaopeng Cui, Shuaicheng Liu, Ping Tan | This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, wh... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RAGO_Recurrent_Graph_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.html | CVPR 2022 | null |
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search | Minbin Huang, Zhijian Huang, Changlin Li, Xin Chen, Hang Xu, Zhenguo Li, Xiaodan Liang | Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network design knowledge across multiple tasks. In this line of research, effectively modeling task correlations is vital yet highly neglected. There... | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Arch-Graph_Acyclic_Architecture_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
On Aliased Resizing and Surprising Subtleties in GAN Evaluation | Gaurav Parmar, Richard Zhang, Jun-Yan Zhu | Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The oftenused Frechet Inception Distance (FID) metric, for example, extracts "high-level" features using a deep network from the two sets. However, we find that the differences in "low-level" preprocessing, specif... | https://openaccess.thecvf.com/content/CVPR2022/papers/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.11222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.html | CVPR 2022 | null |
Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes | Yang Li, Tatsuya Harada | We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D posi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Lepard_Learning_Partial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | CVPR 2022 | null |
Virtual Elastic Objects | Hsiao-yu Chen, Edith Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner | We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then fai... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Virtual_Elastic_Objects_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04623 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.html | CVPR 2022 | null |
DiSparse: Disentangled Sparsification for Multitask Model Compression | Xinglong Sun, Ali Hassani, Zhangyang Wang, Gao Huang, Humphrey Shi | Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, we propose DiSparse, a simple, effective, and first-of-its-kind multitask pruning and sp... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_DiSparse_Disentangled_Sparsification_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales | Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-world few-shot image ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Pushing_the_Limits_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | CVPR 2022 | null |
Opening Up Open World Tracking | Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé | Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world - and yet this is how almost all current systems wor... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Opening_Up_Open_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.11221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Efficient and Scalable Sharpness-Aware Minimization | Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You | Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated a significant performance boost on training large-scale models such as vision transformers. However, the update rule of SAM requires two sequential (non-parallelizable) gradient computati... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Efficient_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02714 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.html | CVPR 2022 | null |
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention | Shengheng Deng, Zhihao Liang, Lin Sun, Kui Jia | Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leverag... | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_VISTA_Boosting_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Deep Face Restoration | Yang Zhao, Yu-Chuan Su, Chun-Te Chu, Yandong Li, Marius Renn, Yukun Zhu, Changyou Chen, Xuhui Jia | A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces. Beca... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Rethinking_Deep_Face_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
OSSO: Obtaining Skeletal Shape From Outside | Marilyn Keller, Silvia Zuffi, Michael J. Black, Sergi Pujades | We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i.e. we predict the inside (bones) from the outside (skin). This has many applications in medicine and biomechanics. Existing state-of-the-art biomechanical skeletons are detailed but do not eas... | https://openaccess.thecvf.com/content/CVPR2022/papers/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Keller_OSSO_Obtaining_Skeletal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.html | CVPR 2022 | null |
Temporal Alignment Networks for Long-Term Video | Tengda Han, Weidi Xie, Andrew Zisserman | The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video; and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale data... | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Temporal_Alignment_Networks_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02968 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Head Swapping in the Wild | Changyong Shu, Hemao Wu, Hang Zhou, Jiaming Liu, Zhibin Hong, Changxing Ding, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention in the community, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherentl... | https://openaccess.thecvf.com/content/CVPR2022/papers/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shu_Few-Shot_Head_Swapping_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.13100 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
A Study on the Distribution of Social Biases in Self-Supervised Learning Visual Models | Kirill Sirotkin, Pablo Carballeira, Marcos Escudero-Viñolo | Deep neural networks are efficient at learning the data distribution if it is sufficiently sampled. However, they can be strongly biased by non-relevant factors implicitly incorporated in the training data. These include operational biases, such as ineffective or uneven data sampling, but also ethical concerns, as the ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sirotkin_A_Study_on_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
LAR-SR: A Local Autoregressive Model for Image Super-Resolution | Baisong Guo, Xiaoyun Zhang, Haoning Wu, Yu Wang, Ya Zhang, Yan-Feng Wang | Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_LAR-SR_A_Local_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
Bayesian Invariant Risk Minimization | Yong Lin, Hanze Dong, Hao Wang, Tong Zhang | Generalization under distributional shift is an open challenge for machine learning. Invariant Risk Minimization (IRM) is a promising framework to tackle this issue by extracting invariant features. However, despite the potential and popularity of IRM, recent works have reported negative results of it on deep models. W... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Bayesian_Invariant_Risk_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.html | CVPR 2022 | null |
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection | Siyue Yu, Jimin Xiao, Bingfeng Zhang, Eng Gee Lim | Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Democracy_Does_Matter_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05787 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint | Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao | Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may di... | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Alleviating_Semantics_Distortion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.html | CVPR 2022 | null |
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches | Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Subhadeep Koley, Rohit Kundu, Aneeshan Sain, Tao Xiang, Yi-Zhe Song | The human visual system is remarkable in learning new visual concepts from just a few examples. This is precisely the goal behind few-shot class incremental learning (FSCIL), where the emphasis is additionally placed on ensuring the model does not suffer from "forgetting". In this paper, we push the boundary further fo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhunia_Doodle_It_Yourself_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14843 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.html | CVPR 2022 | null |
ICON: Implicit Clothed Humans Obtained From Normals | Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, Michael J. Black | Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn the avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiu_ICON_Implicit_Clothed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09127 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | CVPR 2022 | null |
Comparing Correspondences: Video Prediction With Correspondence-Wise Losses | Daniel Geng, Max Hamilton, Andrew Owens | Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to... | https://openaccess.thecvf.com/content/CVPR2022/papers/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.09498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.html | CVPR 2022 | null |
Uni-Perceiver: Pre-Training Unified Architecture for Generic Perception for Zero-Shot and Few-Shot Tasks | Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, Jifeng Dai | Biological intelligence systems of animals perceive the world by integrating information in different modalities and processing simultaneously for various tasks. In contrast, current machine learning research follows a task-specific paradigm, leading to inefficient collaboration between tasks and high marginal costs of... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Uni-Perceiver_Pre-Training_Unified_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html | CVPR 2022 | null |
The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift | Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang | Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world problems at global scale, we need bench... | https://openaccess.thecvf.com/content/CVPR2022/papers/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Beery_The_Auto_Arborist_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html | CVPR 2022 | null |
On the Instability of Relative Pose Estimation and RANSAC's Role | Hongyi Fan, Joe Kileel, Benjamin Kimia | Relative pose estimation using the 5-point or 7-point Random Sample Consensus (RANSAC) algorithms can fail even when no outliers are present and there are enough inliers to support a hypothesis. These cases arise due to numerical instability of the 5- and 7-point minimal problems. This paper characterizes these instabi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_On_the_Instability_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.html | CVPR 2022 | null |
Shape From Polarization for Complex Scenes in the Wild | Chenyang Lei, Chenyang Qi, Jiaxin Xie, Na Fan, Vladlen Koltun, Qifeng Chen | We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. Existing shape from polarization (SfP) works mainly focus on estimating the normal of a single object rather than complex scenes in the wild. A key barrier to high-quality scene-level SfP i... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.11377 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device | Hyojin Park, Alan Yessenbayev, Tushar Singhal, Navin Kumar Adhikari, Yizhe Zhang, Shubhankar Mangesh Borse, Hong Cai, Nilesh Prasad Pandey, Fei Yin, Frank Mayer, Balaji Calidas, Fatih Porikli | This demonstration showcases our innovations on efficient, accurate, and temporally consistent video semantic segmentation on mobile device. We employ our test-time unsupervised scheme, AuxAdapt, to enable the segmentation model to adapt to a given video in an online manner. More specifically, we leverage a small auxil... | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Real-Time_Accurate_and_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
SNUG: Self-Supervised Neural Dynamic Garments | Igor Santesteban, Miguel A. Otaduy, Dan Casas | We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods o... | https://openaccess.thecvf.com/content/CVPR2022/papers/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Santesteban_SNUG_Self-Supervised_Neural_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.02219 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation | Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng | Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, realistic segmentation datasets are highly imbalanced, pseudo labels are typically biased to the majority classes and basically noisy, leadin... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Towards_Fewer_Annotations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12940 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Glass Segmentation Using Intensity and Spectral Polarization Cues | Haiyang Mei, Bo Dong, Wen Dong, Jiaxi Yang, Seung-Hwan Baek, Felix Heide, Pieter Peers, Xiaopeng Wei, Xin Yang | Transparent and semi-transparent materials pose significant challenges for existing scene understanding and segmentation algorithms due to their lack of RGB texture which impedes the extraction of meaningful features. In this work, we exploit that the light-matter interactions on glass materials provide unique intensit... | https://openaccess.thecvf.com/content/CVPR2022/papers/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mei_Glass_Segmentation_Using_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | CVPR 2022 | null |
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding | Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thilakarathna, Ranga Rodrigo | Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.00680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment | Jiayu Xiao, Liang Li, Chaofei Wang, Zheng-Jun Zha, Qingming Huang | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.04121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.html | CVPR 2022 | null |
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection | Jiaxi Wu, Jiaxin Chen, Mengzhe He, Yiru Wang, Bo Li, Bingqi Ma, Weihao Gan, Wei Wu, Yali Wang, Di Huang | Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. T... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Target-Relevant_Knowledge_Preservation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection | Chenxi Xie, Changqun Xia, Mingcan Ma, Zhirui Zhao, Xiaowu Chen, Jia Li | Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at res... | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Pyramid_Grafting_Network_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
A Style-Aware Discriminator for Controllable Image Translation | Kunhee Kim, Sanghun Park, Eunyeong Jeon, Taehun Kim, Daijin Kim | Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largely arises because labels do not consider the semantic distance. To mitigate such problems, we propose... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_A_Style-Aware_Discriminator_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Non-Iterative Recovery From Nonlinear Observations Using Generative Models | Jiulong Liu, Zhaoqiang Liu | In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike for conventional compressed sensing where the signal is assumed to be sparse, we assume that the signal lies in the range of an L-Lipschitz continuous ge... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Non-Iterative_Recovery_From_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.15749 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis | Chaowei Fang, Liang Wang, Dingwen Zhang, Jun Xu, Yixuan Yuan, Junwei Han | Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low within-slice resolution. Improving the inter-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper build... | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.10325 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Enhancing Adversarial Training With Second-Order Statistics of Weights | Gaojie Jin, Xinping Yi, Wei Huang, Sven Schewe, Xiaowei Huang | Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the weights can be optimized through gradient descent methods like SGD. In this paper, we s... | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Enhancing_Adversarial_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06020 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.html | CVPR 2022 | null |
Partially Does It: Towards Scene-Level FG-SBIR With Partial Input | Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Viswanatha Reddy Gajjala, Aneeshan Sain, Tao Xiang, Yi-Zhe Song | We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial". A quick pilot study reveals: (i) a scene sketch does not necessarily contain all objects in the corresponding photo, due to the subjective holistic interpretation of scenes, (ii) th... | https://openaccess.thecvf.com/content/CVPR2022/papers/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chowdhury_Partially_Does_It_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14804 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | CVPR 2022 | null |
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo | Chaoning Zhang, Kang Zhang, Trung X. Pham, Axi Niu, Zhinan Qiao, Chang D. Yoo, In So Kweon | Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS). To decouple the NSS from the MBS, a dynamic dictionary has be... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Dual_Temperature_Helps_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.html | CVPR 2022 | null |
Moving Window Regression: A Novel Approach to Ordinal Regression | Nyeong-Ho Shin, Seon-Ho Lee, Chang-Su Kim | A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (rho-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (rho-regressors) to pred... | https://openaccess.thecvf.com/content/CVPR2022/papers/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shin_Moving_Window_Regression_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
UniCoRN: A Unified Conditional Image Repainting Network | Jimeng Sun, Shuchen Weng, Zheng Chang, Si Li, Boxin Shi | Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly. Existing methods based on two-phase architectu... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Forecasting Characteristic 3D Poses of Human Actions | Christian Diller, Thomas Funkhouser, Angela Dai | We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, characteristic pose - for instance, from observing a person picking up an apple, predict the pose of the person eating the apple. Prior work on ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Diller_Forecasting_Characteristic_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.15079 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.html | CVPR 2022 | null |
ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification | Fengbei Liu, Yu Tian, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, Gustavo Carneiro | Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence).... | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12918 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Learning to Deblur Using Light Field Generated and Real Defocus Images | Lingyan Ruan, Bin Chen, Jizhou Li, Miuling Lam | Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Na... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ruan_Learning_to_Deblur_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00367 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection | Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah | Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked in... | https://openaccess.thecvf.com/content/CVPR2022/papers/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ristea_Self-Supervised_Predictive_Convolutional_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09099 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Safe Self-Refinement for Transformer-Based Domain Adaptation | Tao Sun, Cheng Lu, Tianshuo Zhang, Haibin Ling | Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. In this paper we propose a novel solution named SSRT (Safe Self-Refinement for ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.07683 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
Density-Preserving Deep Point Cloud Compression | Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu | Local density of point clouds is crucial for representing local details, but has been overlooked by existing point cloud compression methods. To address this, we propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsam... | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Density-Preserving_Deep_Point_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions | Lukas Höllein, Justin Johnson, Matthias Nießner | We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patter... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hollein_StyleMesh_Style_Transfer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.html | CVPR 2022 | null |
Which Model To Transfer? Finding the Needle in the Growing Haystack | Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lučić | Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash t... | https://openaccess.thecvf.com/content/CVPR2022/papers/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Renggli_Which_Model_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.06402 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.html | CVPR 2022 | null |
Fast and Unsupervised Action Boundary Detection for Action Segmentation | Zexing Du, Xue Wang, Guoqing Zhou, Qing Wang | To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD). In particular, the proposed method has the following advantages: no training stage and low-latency inference. To detect ac... | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Class-Incremental Learning With Strong Pre-Trained Models | Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, Stefano Soatto | Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can p... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Class-Incremental_Learning_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Optimization As Data Augmentation for Large-Scale Graphs | Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein | Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). While most existing graph regularizers focus on manipulating graph topological structures by adding/r... | https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Robust_Optimization_As_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2010.09891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients | Kaidong Li, Ziming Zhang, Cuncong Zhong, Guanghui Wang | Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15245 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | CVPR 2022 | null |
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes | Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu, Zexiang Xu, Miloš Hašan, Kalyan Sunkavalli, Manmohan Chandraker | Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a phot... | https://openaccess.thecvf.com/content/CVPR2022/papers/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yeh_PhotoScene_Photorealistic_Material_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input | Junyoung Byun, Seungju Cho, Myung-Joon Kwon, Hee-Seon Kim, Changick Kim | The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentatio... | https://openaccess.thecvf.com/content/CVPR2022/papers/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Byun_Improving_the_Transferability_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.html | CVPR 2022 | null |
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials From Photometric Images | Kai Zhang, Fujun Luan, Zhengqi Li, Noah Snavely | We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines. We propose a neural inverse rendering pipeline called IRON that operates on photome... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_IRON_Inverse_Rendering_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02232 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.html | CVPR 2022 | null |
ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer | Ruohan Gao, Zilin Si, Yen-Yu Chang, Samuel Clarke, Jeannette Bohg, Li Fei-Fei, Wenzhen Yuan, Jiajun Wu | Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset that introduces 100 virtualized objects with visual, auditory, and tactile sensory data.... | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Versatile Multi-Modal Pre-Training for Human-Centric Perception | Fangzhou Hong, Liang Pan, Zhongang Cai, Ziwei Liu | Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pre-train model that serves as a foundation for data-efficient downstream tasks transfer. To this end, we propose the Human-Centric Multi-Modal Contr... | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13815 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | CVPR 2022 | null |
360MonoDepth: High-Resolution 360deg Monocular Depth Estimation | Manuel Rey-Area, Mingze Yuan, Christian Richardt | 360deg cameras can capture complete environments in a single shot, which makes 360deg imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360deg data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and ... | https://openaccess.thecvf.com/content/CVPR2022/papers/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rey-Area_360MonoDepth_High-Resolution_360deg_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Splicing ViT Features for Semantic Appearance Transfer | Narek Tumanyan, Omer Bar-Tal, Shai Bagon, Tali Dekel | We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image. Our method wor... | https://openaccess.thecvf.com/content/CVPR2022/papers/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Regression for Domain Adaptation on Gaze Estimation | Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, Teng Li | Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large domain gap hinders the performance of trained mo... | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Contrastive_Regression_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction | Mihee Lee, Samuel S. Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic | Accurate long-term trajectory prediction in complex scenes, where multiple agents (e.g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem. In this work, we propose MUSE-VAE, a new probabi... | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_MUSE-VAE_Multi-Scale_VAE_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis | Xuanmeng Zhang, Zhedong Zheng, Daiheng Gao, Bang Zhang, Pan Pan, Yi Yang | 3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. To address this challenge, we propose Multi-View Consistent Gen... | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Multi-View_Consistent_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06307 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Putting People in Their Place: Monocular Regression of 3D People in Depth | Yu Sun, Wu Liu, Qian Bao, Yili Fu, Tao Mei, Michael J. Black | Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of ver... | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Putting_People_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.08274 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | CVPR 2022 | null |
POCO: Point Convolution for Surface Reconstruction | Alexandre Boulch, Renaud Marlet | Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface function of a whole object or scene into a single latent vector. To overcome this limitation, a few approaches infer latent vectors on a coarse r... | https://openaccess.thecvf.com/content/CVPR2022/papers/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Boulch_POCO_Point_Convolution_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.01831 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
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