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Xing_End-to-End_Learning_for_Joint_Image_Demosaicing_Denoising_and_Super-Resolution_CVPR_2021_paper | End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution | [
"Wenzhu Xing",
"Karen Egiazarian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xing_End-to-End_Learning_for_Joint_Image_Demosaicing_Denoising_and_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xing_End-to-End_Learning_for_Joint_Image_Demosaicing_Denoising_and_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xing_End-to-End_Learning_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Xing_2021_CVPR,
author = {Xing, Wenzhu and Egiazarian, Karen},
title = {End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Image denoising, demosaicing and super-resolution are key problems of image restoration well studied in the recent decades. Often, in practice, one has to solve these problems simultaneously. A problem of finding a joint solution of the multiple image restoration tasks just begun to attract an increased attention of re... |
Tabelini_Keep_Your_Eyes_on_the_Lane_Real-Time_Attention-Guided_Lane_Detection_CVPR_2021_paper | Keep Your Eyes on the Lane: Real-Time Attention-Guided Lane Detection | [
"Lucas Tabelini",
"Rodrigo Berriel",
"Thiago M. Paixao",
"Claudine Badue",
"Alberto F. De Souza",
"Thiago Oliveira-Santos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tabelini_Keep_Your_Eyes_on_the_Lane_Real-Time_Attention-Guided_Lane_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tabelini_Keep_Your_Eyes_on_the_Lane_Real-Time_Attention-Guided_Lane_Detection_CVPR_2021_paper.pdf | null | 2010.12035 | cvf | @InProceedings{Tabelini_2021_CVPR,
author = {Tabelini, Lucas and Berriel, Rodrigo and Paixao, Thiago M. and Badue, Claudine and De Souza, Alberto F. and Oliveira-Santos, Thiago},
title = {Keep Your Eyes on the Lane: Real-Time Attention-Guided Lane Detection},
booktitle = {Proceedings of the IEEE/CVF ... | Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles. In this work, we propose LaneATT: an anchor-based deep lane detection model, which, akin to other generic deep object de... |
Sun_Lesion-Aware_Transformers_for_Diabetic_Retinopathy_Grading_CVPR_2021_paper | Lesion-Aware Transformers for Diabetic Retinopathy Grading | [
"Rui Sun",
"Yihao Li",
"Tianzhu Zhang",
"Zhendong Mao",
"Feng Wu",
"Yongdong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Lesion-Aware_Transformers_for_Diabetic_Retinopathy_Grading_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Lesion-Aware_Transformers_for_Diabetic_Retinopathy_Grading_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Lesion-Aware_Transformers_for_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Sun_2021_CVPR,
author = {Sun, Rui and Li, Yihao and Zhang, Tianzhu and Mao, Zhendong and Wu, Feng and Zhang, Yongdong},
title = {Lesion-Aware Transformers for Diabetic Retinopathy Grading},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Diabetic retinopathy (DR) is the leading cause of permanent blindness in the working-age population. And automatic DR diagnosis can assist ophthalmologists to design tailored treatments for patients, including DR grading and lesion discovery. However, most of existing methods treat DR grading and lesion discovery as tw... |
Li_Involution_Inverting_the_Inherence_of_Convolution_for_Visual_Recognition_CVPR_2021_paper | Involution: Inverting the Inherence of Convolution for Visual Recognition | [
"Duo Li",
"Jie Hu",
"Changhu Wang",
"Xiangtai Li",
"Qi She",
"Lei Zhu",
"Tong Zhang",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Involution_Inverting_the_Inherence_of_Convolution_for_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Involution_Inverting_the_Inherence_of_Convolution_for_Visual_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Involution_Inverting_the_CVPR_2021_supplemental.pdf | 2103.06255 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Duo and Hu, Jie and Wang, Changhu and Li, Xiangtai and She, Qi and Zhu, Lei and Zhang, Tong and Chen, Qifeng},
title = {Involution: Inverting the Inherence of Convolution for Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Compute... | Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel-specific. Instead, we present a novel atomic operation for deep neural... |
Tamura_QPIC_Query-Based_Pairwise_Human-Object_Interaction_Detection_With_Image-Wide_Contextual_Information_CVPR_2021_paper | QPIC: Query-Based Pairwise Human-Object Interaction Detection With Image-Wide Contextual Information | [
"Masato Tamura",
"Hiroki Ohashi",
"Tomoaki Yoshinaga"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tamura_QPIC_Query-Based_Pairwise_Human-Object_Interaction_Detection_With_Image-Wide_Contextual_Information_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tamura_QPIC_Query-Based_Pairwise_Human-Object_Interaction_Detection_With_Image-Wide_Contextual_Information_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tamura_QPIC_Query-Based_Pairwise_CVPR_2021_supplemental.pdf | 2103.05399 | cvf | @InProceedings{Tamura_2021_CVPR,
author = {Tamura, Masato and Ohashi, Hiroki and Yoshinaga, Tomoaki},
title = {QPIC: Query-Based Pairwise Human-Object Interaction Detection With Image-Wide Contextual Information},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based methods face the following three major drawbacks; they cannot leverage image-wide features due to CNN's locality, they rely on a manually defin... |
Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper | Home Action Genome: Cooperative Compositional Action Understanding | [
"Nishant Rai",
"Haofeng Chen",
"Jingwei Ji",
"Rishi Desai",
"Kazuki Kozuka",
"Shun Ishizaka",
"Ehsan Adeli",
"Juan Carlos Niebles"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Rai_Home_Action_Genome_CVPR_2021_supplemental.zip | 2105.05226 | cvf | @InProceedings{Rai_2021_CVPR,
author = {Rai, Nishant and Chen, Haofeng and Ji, Jingwei and Desai, Rishi and Kozuka, Kazuki and Ishizaka, Shun and Adeli, Ehsan and Niebles, Juan Carlos},
title = {Home Action Genome: Cooperative Compositional Action Understanding},
booktitle = {Proceedings of the IEEE/... | Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomic-actions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn repre... |
Cai_Deep_Lesion_Tracker_Monitoring_Lesions_in_4D_Longitudinal_Imaging_Studies_CVPR_2021_paper | Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies | [
"Jinzheng Cai",
"Youbao Tang",
"Ke Yan",
"Adam P. Harrison",
"Jing Xiao",
"Gigin Lin",
"Le Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cai_Deep_Lesion_Tracker_Monitoring_Lesions_in_4D_Longitudinal_Imaging_Studies_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Deep_Lesion_Tracker_Monitoring_Lesions_in_4D_Longitudinal_Imaging_Studies_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Cai_Deep_Lesion_Tracker_CVPR_2021_supplemental.pdf | 2012.04872 | cvf | @InProceedings{Cai_2021_CVPR,
author = {Cai, Jinzheng and Tang, Youbao and Yan, Ke and Harrison, Adam P. and Xiao, Jing and Lin, Gigin and Lu, Le},
title = {Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and anatomical considerations. However, matching lesions manually is labor-in... |
Liu_Learning_To_Warp_for_Style_Transfer_CVPR_2021_paper | Learning To Warp for Style Transfer | [
"Xiao-Chang Liu",
"Yong-Liang Yang",
"Peter Hall"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Learning_To_Warp_for_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Learning_To_Warp_for_Style_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Learning_To_Warp_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Liu_2021_CVPR,
author = {Liu, Xiao-Chang and Yang, Yong-Liang and Hall, Peter},
title = {Learning To Warp for Style Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pag... | Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of int... |
Yin_Towards_Extremely_Compact_RNNs_for_Video_Recognition_With_Fully_Decomposed_CVPR_2021_paper | Towards Extremely Compact RNNs for Video Recognition With Fully Decomposed Hierarchical Tucker Structure | [
"Miao Yin",
"Siyu Liao",
"Xiao-Yang Liu",
"Xiaodong Wang",
"Bo Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Towards_Extremely_Compact_RNNs_for_Video_Recognition_With_Fully_Decomposed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Towards_Extremely_Compact_RNNs_for_Video_Recognition_With_Fully_Decomposed_CVPR_2021_paper.pdf | null | 2104.05758 | cvf | @InProceedings{Yin_2021_CVPR,
author = {Yin, Miao and Liao, Siyu and Liu, Xiao-Yang and Wang, Xiaodong and Yuan, Bo},
title = {Towards Extremely Compact RNNs for Video Recognition With Fully Decomposed Hierarchical Tucker Structure},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, exec... |
Hur_Self-Supervised_Multi-Frame_Monocular_Scene_Flow_CVPR_2021_paper | Self-Supervised Multi-Frame Monocular Scene Flow | [
"Junhwa Hur",
"Stefan Roth"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hur_Self-Supervised_Multi-Frame_Monocular_Scene_Flow_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hur_Self-Supervised_Multi-Frame_Monocular_Scene_Flow_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hur_Self-Supervised_Multi-Frame_Monocular_CVPR_2021_supplemental.pdf | 2105.02216 | cvf | @InProceedings{Hur_2021_CVPR,
author = {Hur, Junhwa and Roth, Stefan},
title = {Self-Supervised Multi-Frame Monocular Scene Flow},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {2... | Estimating 3D scene flow from a sequence of monocular images has been gaining increased attention due to the simple, economical capture setup. Owing to the severe ill-posedness of the problem, the accuracy of current methods has been limited, especially that of efficient, real-time approaches. In this paper, we introdu... |
Roads_Enriching_ImageNet_With_Human_Similarity_Judgments_and_Psychological_Embeddings_CVPR_2021_paper | Enriching ImageNet With Human Similarity Judgments and Psychological Embeddings | [
"Brett D. Roads",
"Bradley C. Love"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Roads_Enriching_ImageNet_With_Human_Similarity_Judgments_and_Psychological_Embeddings_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Roads_Enriching_ImageNet_With_Human_Similarity_Judgments_and_Psychological_Embeddings_CVPR_2021_paper.pdf | null | 2011.11015 | cvf | @InProceedings{Roads_2021_CVPR,
author = {Roads, Brett D. and Love, Bradley C.},
title = {Enriching ImageNet With Human Similarity Judgments and Psychological Embeddings},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks---such as ILSVRC---are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-availa... |
Bahat_Whats_in_the_Image_Explorable_Decoding_of_Compressed_Images_CVPR_2021_paper | What's in the Image? Explorable Decoding of Compressed Images | [
"Yuval Bahat",
"Tomer Michaeli"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bahat_Whats_in_the_Image_Explorable_Decoding_of_Compressed_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bahat_Whats_in_the_Image_Explorable_Decoding_of_Compressed_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bahat_Whats_in_the_CVPR_2021_supplemental.pdf | 2006.09332 | title_snapshot | @InProceedings{Bahat_2021_CVPR,
author = {Bahat, Yuval and Michaeli, Tomer},
title = {What's in the Image? Explorable Decoding of Compressed Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},... | The ever-growing amounts of visual contents captured on a daily basis necessitate the use of lossy compression methods in order to save storage space and transmission bandwidth. While extensive research efforts are devoted to improving compression techniques, every method inevitably discards information. Especially at ... |
Ma_Context_Modeling_in_3D_Human_Pose_Estimation_A_Unified_Perspective_CVPR_2021_paper | Context Modeling in 3D Human Pose Estimation: A Unified Perspective | [
"Xiaoxuan Ma",
"Jiajun Su",
"Chunyu Wang",
"Hai Ci",
"Yizhou Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ma_Context_Modeling_in_3D_Human_Pose_Estimation_A_Unified_Perspective_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ma_Context_Modeling_in_3D_Human_Pose_Estimation_A_Unified_Perspective_CVPR_2021_paper.pdf | null | 2103.15507 | cvf | @InProceedings{Ma_2021_CVPR,
author = {Ma, Xiaoxuan and Su, Jiajun and Wang, Chunyu and Ci, Hai and Wang, Yizhou},
title = {Context Modeling in 3D Human Pose Estimation: A Unified Perspective},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there... |
Lei_Less_Is_More_ClipBERT_for_Video-and-Language_Learning_via_Sparse_Sampling_CVPR_2021_paper | Less Is More: ClipBERT for Video-and-Language Learning via Sparse Sampling | [
"Jie Lei",
"Linjie Li",
"Luowei Zhou",
"Zhe Gan",
"Tamara L. Berg",
"Mohit Bansal",
"Jingjing Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lei_Less_Is_More_ClipBERT_for_Video-and-Language_Learning_via_Sparse_Sampling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lei_Less_Is_More_ClipBERT_for_Video-and-Language_Learning_via_Sparse_Sampling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lei_Less_Is_More_CVPR_2021_supplemental.pdf | 2102.06183 | cvf | @InProceedings{Lei_2021_CVPR,
author = {Lei, Jie and Li, Linjie and Zhou, Luowei and Gan, Zhe and Berg, Tamara L. and Bansal, Mohit and Liu, Jingjing},
title = {Less Is More: ClipBERT for Video-and-Language Learning via Sparse Sampling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision models and text features from language models. These feature extractors are trained independently and usually on tasks different from the target... |
Tennakoon_Consensus_Maximisation_Using_Influences_of_Monotone_Boolean_Functions_CVPR_2021_paper | Consensus Maximisation Using Influences of Monotone Boolean Functions | [
"Ruwan Tennakoon",
"David Suter",
"Erchuan Zhang",
"Tat-Jun Chin",
"Alireza Bab-Hadiashar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tennakoon_Consensus_Maximisation_Using_Influences_of_Monotone_Boolean_Functions_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tennakoon_Consensus_Maximisation_Using_Influences_of_Monotone_Boolean_Functions_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tennakoon_Consensus_Maximisation_Using_CVPR_2021_supplemental.pdf | 2103.04200 | cvf | @InProceedings{Tennakoon_2021_CVPR,
author = {Tennakoon, Ruwan and Suter, David and Zhang, Erchuan and Chin, Tat-Jun and Bab-Hadiashar, Alireza},
title = {Consensus Maximisation Using Influences of Monotone Boolean Functions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Consensus maximisation (MaxCon), widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level. In this paper, we outline the connection between MaxCon problem and the abstract problem of finding the maximum upper zero of a Monotone Boolean Fu... |
Li_Meta-Mining_Discriminative_Samples_for_Kinship_Verification_CVPR_2021_paper | Meta-Mining Discriminative Samples for Kinship Verification | [
"Wanhua Li",
"Shiwei Wang",
"Jiwen Lu",
"Jianjiang Feng",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Meta-Mining_Discriminative_Samples_for_Kinship_Verification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Meta-Mining_Discriminative_Samples_for_Kinship_Verification_CVPR_2021_paper.pdf | null | 2103.15108 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Wanhua and Wang, Shiwei and Lu, Jiwen and Feng, Jianjiang and Zhou, Jie},
title = {Meta-Mining Discriminative Samples for Kinship Verification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Kinship verification aims to find out whether there is a kin relation for a given pair of facial images. Kinship verification databases are born with unbalanced data. For a database with N positive kinship pairs, we naturally obtain N(N-1) negative pairs. How to fully utilize the limited positive pairs and mine discrim... |
Chen_AQD_Towards_Accurate_Quantized_Object_Detection_CVPR_2021_paper | AQD: Towards Accurate Quantized Object Detection | [
"Peng Chen",
"Jing Liu",
"Bohan Zhuang",
"Mingkui Tan",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_AQD_Towards_Accurate_Quantized_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_AQD_Towards_Accurate_Quantized_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_AQD_Towards_Accurate_CVPR_2021_supplemental.pdf | 2007.06919 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Peng and Liu, Jing and Zhuang, Bohan and Tan, Mingkui and Shen, Chunhua},
title = {AQD: Towards Accurate Quantized Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices. However, designing aggressively low-bit (e.g., 2-bit) quantization schemes on complex tasks, such as object detection, still remains challenging in terms of sev... |
Hu_Learning_Cross-Modal_Retrieval_With_Noisy_Labels_CVPR_2021_paper | Learning Cross-Modal Retrieval With Noisy Labels | [
"Peng Hu",
"Xi Peng",
"Hongyuan Zhu",
"Liangli Zhen",
"Jie Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Learning_Cross-Modal_Retrieval_With_Noisy_Labels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Learning_Cross-Modal_Retrieval_With_Noisy_Labels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hu_Learning_Cross-Modal_Retrieval_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Hu_2021_CVPR,
author = {Hu, Peng and Peng, Xi and Zhu, Hongyuan and Zhen, Liangli and Lin, Jie},
title = {Learning Cross-Modal Retrieval With Noisy Labels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Recently, cross-modal retrieval is emerging with the help of deep multimodal learning. However, even for unimodal data, collecting large-scale well-annotated data is expensive and time-consuming, and not to mention the additional challenges from multiple modalities. Although crowd-sourcing annotation, e.g., Amazon's Me... |
Saha_LOHO_Latent_Optimization_of_Hairstyles_via_Orthogonalization_CVPR_2021_paper | LOHO: Latent Optimization of Hairstyles via Orthogonalization | [
"Rohit Saha",
"Brendan Duke",
"Florian Shkurti",
"Graham W. Taylor",
"Parham Aarabi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Saha_LOHO_Latent_Optimization_of_Hairstyles_via_Orthogonalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Saha_LOHO_Latent_Optimization_of_Hairstyles_via_Orthogonalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saha_LOHO_Latent_Optimization_CVPR_2021_supplemental.pdf | 2103.03891 | cvf | @InProceedings{Saha_2021_CVPR,
author = {Saha, Rohit and Duke, Brendan and Shkurti, Florian and Taylor, Graham W. and Aarabi, Parham},
title = {LOHO: Latent Optimization of Hairstyles via Orthogonalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Hairstyle transfer is challenging due to hair structure differences in the source and target hair. Therefore, we propose Latent Optimization of Hairstyles via Orthogonalization (LOHO), an optimization-based approach using GAN inversion to infill missing hair structure details in latent space during hairstyle transfer. ... |
Gafni_Single-Shot_Freestyle_Dance_Reenactment_CVPR_2021_paper | Single-Shot Freestyle Dance Reenactment | [
"Oran Gafni",
"Oron Ashual",
"Lior Wolf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gafni_Single-Shot_Freestyle_Dance_Reenactment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gafni_Single-Shot_Freestyle_Dance_Reenactment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gafni_Single-Shot_Freestyle_Dance_CVPR_2021_supplemental.pdf | 2012.01158 | cvf | @InProceedings{Gafni_2021_CVPR,
author = {Gafni, Oran and Ashual, Oron and Wolf, Lior},
title = {Single-Shot Freestyle Dance Reenactment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unse... |
Olsson_A_Quasiconvex_Formulation_for_Radial_Cameras_CVPR_2021_paper | A Quasiconvex Formulation for Radial Cameras | [
"Carl Olsson",
"Viktor Larsson",
"Fredrik Kahl"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Olsson_A_Quasiconvex_Formulation_for_Radial_Cameras_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Olsson_A_Quasiconvex_Formulation_for_Radial_Cameras_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Olsson_2021_CVPR,
author = {Olsson, Carl and Larsson, Viktor and Kahl, Fredrik},
title = {A Quasiconvex Formulation for Radial Cameras},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | In this paper we study structure from motion problems for 1D radial cameras. Under this model the projection of a 3D point is a line in the image plane going through the principal point, which makes the model invariant to radial distortion and changes in focal length. It can therefore effectively be applied to uncalibr... |
Yang_Self-Supervised_Learning_of_Depth_Inference_for_Multi-View_Stereo_CVPR_2021_paper | Self-Supervised Learning of Depth Inference for Multi-View Stereo | [
"Jiayu Yang",
"Jose M. Alvarez",
"Miaomiao Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Self-Supervised_Learning_of_Depth_Inference_for_Multi-View_Stereo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Self-Supervised_Learning_of_Depth_Inference_for_Multi-View_Stereo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Self-Supervised_Learning_of_CVPR_2021_supplemental.pdf | 2104.02972 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Jiayu and Alvarez, Jose M. and Liu, Miaomiao},
title = {Self-Supervised Learning of Depth Inference for Multi-View Stereo},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during training. However, collecting a large amount of multi-view depth data is very challenging. Here, we propose a self-supervised learning framework f... |
Lambourne_BRepNet_A_Topological_Message_Passing_System_for_Solid_Models_CVPR_2021_paper | BRepNet: A Topological Message Passing System for Solid Models | [
"Joseph G. Lambourne",
"Karl D.D. Willis",
"Pradeep Kumar Jayaraman",
"Aditya Sanghi",
"Peter Meltzer",
"Hooman Shayani"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lambourne_BRepNet_A_Topological_Message_Passing_System_for_Solid_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lambourne_BRepNet_A_Topological_Message_Passing_System_for_Solid_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lambourne_BRepNet_A_Topological_CVPR_2021_supplemental.zip | 2104.00706 | cvf | @InProceedings{Lambourne_2021_CVPR,
author = {Lambourne, Joseph G. and Willis, Karl D.D. and Jayaraman, Pradeep Kumar and Sanghi, Aditya and Meltzer, Peter and Shayani, Hooman},
title = {BRepNet: A Topological Message Passing System for Solid Models},
booktitle = {Proceedings of the IEEE/CVF Conferen... | Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surfaces with topological information which connects the geometric entities to describe manifolds. In this paper we introduce BRepNet, a neural ... |
Pham_Learning_To_Predict_Visual_Attributes_in_the_Wild_CVPR_2021_paper | Learning To Predict Visual Attributes in the Wild | [
"Khoi Pham",
"Kushal Kafle",
"Zhe Lin",
"Zhihong Ding",
"Scott Cohen",
"Quan Tran",
"Abhinav Shrivastava"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pham_Learning_To_Predict_Visual_Attributes_in_the_Wild_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pham_Learning_To_Predict_Visual_Attributes_in_the_Wild_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Pham_Learning_To_Predict_CVPR_2021_supplemental.pdf | 2106.09707 | cvf | @InProceedings{Pham_2021_CVPR,
author = {Pham, Khoi and Kafle, Kushal and Lin, Zhe and Ding, Zhihong and Cohen, Scott and Tran, Quan and Shrivastava, Abhinav},
title = {Learning To Predict Visual Attributes in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size, posture), and other intrinsic properties (state, action). Existing work is mostly limited to study ... |
Holynski_Animating_Pictures_With_Eulerian_Motion_Fields_CVPR_2021_paper | Animating Pictures With Eulerian Motion Fields | [
"Aleksander Holynski",
"Brian L. Curless",
"Steven M. Seitz",
"Richard Szeliski"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Holynski_Animating_Pictures_With_Eulerian_Motion_Fields_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Holynski_Animating_Pictures_With_Eulerian_Motion_Fields_CVPR_2021_paper.pdf | null | 2011.15128 | cvf | @InProceedings{Holynski_2021_CVPR,
author = {Holynski, Aleksander and Curless, Brian L. and Seitz, Steven M. and Szeliski, Richard},
title = {Animating Pictures With Eulerian Motion Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from... |
Li_Generalized_Focal_Loss_V2_Learning_Reliable_Localization_Quality_Estimation_for_CVPR_2021_paper | Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection | [
"Xiang Li",
"Wenhai Wang",
"Xiaolin Hu",
"Jun Li",
"Jinhui Tang",
"Jian Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Generalized_Focal_Loss_V2_Learning_Reliable_Localization_Quality_Estimation_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Generalized_Focal_Loss_V2_Learning_Reliable_Localization_Quality_Estimation_for_CVPR_2021_paper.pdf | null | 2011.12885 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
title = {Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vani... |
Li_Cross-Domain_Adaptive_Clustering_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper | Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation | [
"Jichang Li",
"Guanbin Li",
"Yemin Shi",
"Yizhou Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Cross-Domain_Adaptive_Clustering_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Cross-Domain_Adaptive_Clustering_for_Semi-Supervised_Domain_Adaptation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Cross-Domain_Adaptive_Clustering_CVPR_2021_supplemental.pdf | 2104.09415 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Jichang and Li, Guanbin and Shi, Yemin and Yu, Yizhou},
title = {Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the trained model cannot produce a highly discriminative feature representation for the target domain because the training data is dominated by lab... |
Yang_ST3D_Self-Training_for_Unsupervised_Domain_Adaptation_on_3D_Object_Detection_CVPR_2021_paper | ST3D: Self-Training for Unsupervised Domain Adaptation on 3D Object Detection | [
"Jihan Yang",
"Shaoshuai Shi",
"Zhe Wang",
"Hongsheng Li",
"Xiaojuan Qi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_ST3D_Self-Training_for_Unsupervised_Domain_Adaptation_on_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_ST3D_Self-Training_for_Unsupervised_Domain_Adaptation_on_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_ST3D_Self-Training_for_CVPR_2021_supplemental.pdf | 2103.05346 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Jihan and Shi, Shaoshuai and Wang, Zhe and Li, Hongsheng and Qi, Xiaojuan},
title = {ST3D: Self-Training for Unsupervised Domain Adaptation on 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds. First, we pre-train the 3D detector on the source domain with our proposed random object scaling strategy for mitigating the negative effects of source domain bias. Then, the... |
Tankovich_HITNet_Hierarchical_Iterative_Tile_Refinement_Network_for_Real-time_Stereo_Matching_CVPR_2021_paper | HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching | [
"Vladimir Tankovich",
"Christian Hane",
"Yinda Zhang",
"Adarsh Kowdle",
"Sean Fanello",
"Sofien Bouaziz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tankovich_HITNet_Hierarchical_Iterative_Tile_Refinement_Network_for_Real-time_Stereo_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tankovich_HITNet_Hierarchical_Iterative_Tile_Refinement_Network_for_Real-time_Stereo_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tankovich_HITNet_Hierarchical_Iterative_CVPR_2021_supplemental.pdf | 2007.12140 | cvf | @InProceedings{Tankovich_2021_CVPR,
author = {Tankovich, Vladimir and Hane, Christian and Zhang, Yinda and Kowdle, Adarsh and Fanello, Sean and Bouaziz, Sofien},
title = {HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching},
booktitle = {Proceedings of the IEEE/CVF Co... | This paper presents HITNet, a novel neural network architecture for real-time stereo matching. Contrary to many recent neural network approaches that operate on a full costvolume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization ste... |
Choi_VaB-AL_Incorporating_Class_Imbalance_and_Difficulty_With_Variational_Bayes_for_CVPR_2021_paper | VaB-AL: Incorporating Class Imbalance and Difficulty With Variational Bayes for Active Learning | [
"Jongwon Choi",
"Kwang Moo Yi",
"Jihoon Kim",
"Jinho Choo",
"Byoungjip Kim",
"Jinyeop Chang",
"Youngjune Gwon",
"Hyung Jin Chang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Choi_VaB-AL_Incorporating_Class_Imbalance_and_Difficulty_With_Variational_Bayes_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_VaB-AL_Incorporating_Class_Imbalance_and_Difficulty_With_Variational_Bayes_for_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Choi_VaB-AL_Incorporating_Class_CVPR_2021_supplemental.pdf | 2003.11249 | title_snapshot | @InProceedings{Choi_2021_CVPR,
author = {Choi, Jongwon and Yi, Kwang Moo and Kim, Jihoon and Choo, Jinho and Kim, Byoungjip and Chang, Jinyeop and Gwon, Youngjune and Chang, Hyung Jin},
title = {VaB-AL: Incorporating Class Imbalance and Difficulty With Variational Bayes for Active Learning},
booktitl... | Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes' rule, that can naturally incorporate clas... |
Raaj_Exploiting__Refining_Depth_Distributions_With_Triangulation_Light_Curtains_CVPR_2021_paper | Exploiting & Refining Depth Distributions With Triangulation Light Curtains | [
"Yaadhav Raaj",
"Siddharth Ancha",
"Robert Tamburo",
"David Held",
"Srinivasa G. Narasimhan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Raaj_Exploiting__Refining_Depth_Distributions_With_Triangulation_Light_Curtains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Raaj_Exploiting__Refining_Depth_Distributions_With_Triangulation_Light_Curtains_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Raaj_2021_CVPR,
author = {Raaj, Yaadhav and Ancha, Siddharth and Tamburo, Robert and Held, David and Narasimhan, Srinivasa G.},
title = {Exploiting \& Refining Depth Distributions With Triangulation Light Curtains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS). They do however require dynamically driving a laser / light-source to a specific location to capture information, with one such class of sensor being the Triangulation ... |
Xie_DG-Font_Deformable_Generative_Networks_for_Unsupervised_Font_Generation_CVPR_2021_paper | DG-Font: Deformable Generative Networks for Unsupervised Font Generation | [
"Yangchen Xie",
"Xinyuan Chen",
"Li Sun",
"Yue Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_DG-Font_Deformable_Generative_Networks_for_Unsupervised_Font_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_DG-Font_Deformable_Generative_Networks_for_Unsupervised_Font_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xie_DG-Font_Deformable_Generative_CVPR_2021_supplemental.pdf | 2104.03064 | title_snapshot | @InProceedings{Xie_2021_CVPR,
author = {Xie, Yangchen and Chen, Xinyuan and Sun, Li and Lu, Yue},
title = {DG-Font: Deformable Generative Networks for Unsupervised Font Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years. However, existing methods for font generation are often in supervised learning. They require a large number of paired data, which is labor-inten... |
Phillips_Deep_Multi-Task_Learning_for_Joint_Localization_Perception_and_Prediction_CVPR_2021_paper | Deep Multi-Task Learning for Joint Localization, Perception, and Prediction | [
"John Phillips",
"Julieta Martinez",
"Ioan Andrei Barsan",
"Sergio Casas",
"Abbas Sadat",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Phillips_Deep_Multi-Task_Learning_for_Joint_Localization_Perception_and_Prediction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Phillips_Deep_Multi-Task_Learning_for_Joint_Localization_Perception_and_Prediction_CVPR_2021_paper.pdf | null | 2101.06720 | cvf | @InProceedings{Phillips_2021_CVPR,
author = {Phillips, John and Martinez, Julieta and Barsan, Ioan Andrei and Casas, Sergio and Sadat, Abbas and Urtasun, Raquel},
title = {Deep Multi-Task Learning for Joint Localization, Perception, and Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference... | Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving including perception, motion forecasting, and motion planning. However, these systems often assume that the car is accurately localized against a high-definition map. In this paper we question this assumption, and inve... |
Ding_Deeply_Shape-Guided_Cascade_for_Instance_Segmentation_CVPR_2021_paper | Deeply Shape-Guided Cascade for Instance Segmentation | [
"Hao Ding",
"Siyuan Qiao",
"Alan Yuille",
"Wei Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_Deeply_Shape-Guided_Cascade_for_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_Deeply_Shape-Guided_Cascade_for_Instance_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ding_Deeply_Shape-Guided_Cascade_CVPR_2021_supplemental.pdf | 1911.11263 | cvf | @InProceedings{Ding_2021_CVPR,
author = {Ding, Hao and Qiao, Siyuan and Yuille, Alan and Shen, Wei},
title = {Deeply Shape-Guided Cascade for Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | The key to a successful cascade architecture for precise instance segmentation is to fully leverage the relationship between bounding box detection and mask segmentation across multiple stages. Although modern instance segmentation cascades achieve leading performance, they mainly make use of a unidirectional relations... |
Huang_MetricOpt_Learning_To_Optimize_Black-Box_Evaluation_Metrics_CVPR_2021_paper | MetricOpt: Learning To Optimize Black-Box Evaluation Metrics | [
"Chen Huang",
"Shuangfei Zhai",
"Pengsheng Guo",
"Josh Susskind"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_MetricOpt_Learning_To_Optimize_Black-Box_Evaluation_Metrics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_MetricOpt_Learning_To_Optimize_Black-Box_Evaluation_Metrics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_MetricOpt_Learning_To_CVPR_2021_supplemental.pdf | 2104.10631 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Chen and Zhai, Shuangfei and Guo, Pengsheng and Susskind, Josh},
title = {MetricOpt: Learning To Optimize Black-Box Evaluation Metrics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We study the problem of directly optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in a black-box setting where the computational details of the target metric are unknown. We achieve this by learning a differentiable value fu... |
Guo_Multispectral_Photometric_Stereo_for_Spatially-Varying_Spectral_Reflectances_A_Well_Posed_CVPR_2021_paper | Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A Well Posed Problem? | [
"Heng Guo",
"Fumio Okura",
"Boxin Shi",
"Takuya Funatomi",
"Yasuhiro Mukaigawa",
"Yasuyuki Matsushita"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Multispectral_Photometric_Stereo_for_Spatially-Varying_Spectral_Reflectances_A_Well_Posed_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Multispectral_Photometric_Stereo_for_Spatially-Varying_Spectral_Reflectances_A_Well_Posed_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guo_Multispectral_Photometric_Stereo_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Guo_2021_CVPR,
author = {Guo, Heng and Okura, Fumio and Shi, Boxin and Funatomi, Takuya and Mukaigawa, Yasuhiro and Matsushita, Yasuyuki},
title = {Multispectral Photometric Stereo for Spatially-Varying Spectral Reflectances: A Well Posed Problem?},
booktitle = {Proceedings of the IEEE... | Multispectral photometric stereo (MPS) aims at recovering the surface normal of a scene from a single-shot multispectral image, which is known as an ill-posed problem. To make the problem well-posed, existing MPS methods rely on restrictive assumptions, such as shape prior, surfaces having a monochromatic with uniform ... |
Wu_Fashion_IQ_A_New_Dataset_Towards_Retrieving_Images_by_Natural_CVPR_2021_paper | Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback | [
"Hui Wu",
"Yupeng Gao",
"Xiaoxiao Guo",
"Ziad Al-Halah",
"Steven Rennie",
"Kristen Grauman",
"Rogerio Feris"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wu_Fashion_IQ_A_New_Dataset_Towards_Retrieving_Images_by_Natural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wu_Fashion_IQ_A_New_Dataset_Towards_Retrieving_Images_by_Natural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wu_Fashion_IQ_A_CVPR_2021_supplemental.pdf | 1905.12794 | cvf | @InProceedings{Wu_2021_CVPR,
author = {Wu, Hui and Gao, Yupeng and Guo, Xiaoxiao and Al-Halah, Ziad and Rennie, Steven and Grauman, Kristen and Feris, Rogerio},
title = {Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback},
booktitle = {Proceedings of the IEEE/CVF Confere... | Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashi... |
Huang_Few-Shot_Human_Motion_Transfer_by_Personalized_Geometry_and_Texture_Modeling_CVPR_2021_paper | Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling | [
"Zhichao Huang",
"Xintong Han",
"Jia Xu",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huang_Few-Shot_Human_Motion_Transfer_by_Personalized_Geometry_and_Texture_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huang_Few-Shot_Human_Motion_Transfer_by_Personalized_Geometry_and_Texture_Modeling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huang_Few-Shot_Human_Motion_CVPR_2021_supplemental.pdf | 2103.14338 | cvf | @InProceedings{Huang_2021_CVPR,
author = {Huang, Zhichao and Han, Xintong and Xu, Jia and Zhang, Tong},
title = {Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. Despite recent advances in single person motion transfer, prior methods often require a large number of training images and take long training time. One promising dire... |
Mi_HDMapGen_A_Hierarchical_Graph_Generative_Model_of_High_Definition_Maps_CVPR_2021_paper | HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps | [
"Lu Mi",
"Hang Zhao",
"Charlie Nash",
"Xiaohan Jin",
"Jiyang Gao",
"Chen Sun",
"Cordelia Schmid",
"Nir Shavit",
"Yuning Chai",
"Dragomir Anguelov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mi_HDMapGen_A_Hierarchical_Graph_Generative_Model_of_High_Definition_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mi_HDMapGen_A_Hierarchical_Graph_Generative_Model_of_High_Definition_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mi_HDMapGen_A_Hierarchical_CVPR_2021_supplemental.pdf | 2106.14880 | title_snapshot | @InProceedings{Mi_2021_CVPR,
author = {Mi, Lu and Zhao, Hang and Nash, Charlie and Jin, Xiaohan and Gao, Jiyang and Sun, Chen and Schmid, Cordelia and Shavit, Nir and Chai, Yuning and Anguelov, Dragomir},
title = {HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps},
booktitle = {... | High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, wh... |
Chen_GeoSim_Realistic_Video_Simulation_via_Geometry-Aware_Composition_for_Self-Driving_CVPR_2021_paper | GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving | [
"Yun Chen",
"Frieda Rong",
"Shivam Duggal",
"Shenlong Wang",
"Xinchen Yan",
"Sivabalan Manivasagam",
"Shangjie Xue",
"Ersin Yumer",
"Raquel Urtasun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_GeoSim_Realistic_Video_Simulation_via_Geometry-Aware_Composition_for_Self-Driving_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_GeoSim_Realistic_Video_Simulation_via_Geometry-Aware_Composition_for_Self-Driving_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_GeoSim_Realistic_Video_CVPR_2021_supplemental.zip | 2101.06543 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yun and Rong, Frieda and Duggal, Shivam and Wang, Shenlong and Yan, Xinchen and Manivasagam, Sivabalan and Xue, Shangjie and Yumer, Ersin and Urtasun, Raquel},
title = {GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving},
... | Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photorealistic or do not model the 3D environment and the dynamic objects within, losing high-level control and physical realism. In this paper, w... |
Gong_AlphaMatch_Improving_Consistency_for_Semi-Supervised_Learning_With_Alpha-Divergence_CVPR_2021_paper | AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence | [
"Chengyue Gong",
"Dilin Wang",
"Qiang Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_AlphaMatch_Improving_Consistency_for_Semi-Supervised_Learning_With_Alpha-Divergence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_AlphaMatch_Improving_Consistency_for_Semi-Supervised_Learning_With_Alpha-Divergence_CVPR_2021_paper.pdf | null | 2011.11779 | cvf | @InProceedings{Gong_2021_CVPR,
author = {Gong, Chengyue and Wang, Dilin and Liu, Qiang},
title = {AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data. We propose AlphaMatch, an efficient SSL method that leverages data augmentations, by efficiently enforcing the label consistency between the data points and the augmented dat... |
Zhan_Unbalanced_Feature_Transport_for_Exemplar-Based_Image_Translation_CVPR_2021_paper | Unbalanced Feature Transport for Exemplar-Based Image Translation | [
"Fangneng Zhan",
"Yingchen Yu",
"Kaiwen Cui",
"Gongjie Zhang",
"Shijian Lu",
"Jianxiong Pan",
"Changgong Zhang",
"Feiying Ma",
"Xuansong Xie",
"Chunyan Miao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhan_Unbalanced_Feature_Transport_for_Exemplar-Based_Image_Translation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhan_Unbalanced_Feature_Transport_for_Exemplar-Based_Image_Translation_CVPR_2021_paper.pdf | null | 2106.10482 | cvf | @InProceedings{Zhan_2021_CVPR,
author = {Zhan, Fangneng and Yu, Yingchen and Cui, Kaiwen and Zhang, Gongjie and Lu, Shijian and Pan, Jianxiong and Zhang, Changgong and Ma, Feiying and Xie, Xuansong and Miao, Chunyan},
title = {Unbalanced Feature Transport for Exemplar-Based Image Translation},
bookti... | Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge map, generating high-fidelity images with reference styles from exemplars remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation ... |
Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper | Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators | [
"Wenda Zhao",
"Cai Shang",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Self-Generated_Defocus_Blur_Detection_via_Dual_Adversarial_Discriminators_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Wenda and Shang, Cai and Lu, Huchuan},
title = {Self-Generated Defocus Blur Detection via Dual Adversarial Discriminators},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and error-prone. Addressing this issue, this paper makes an effort to train a deep DBD model without using a... |
Bhattad_View_Generalization_for_Single_Image_Textured_3D_Models_CVPR_2021_paper | View Generalization for Single Image Textured 3D Models | [
"Anand Bhattad",
"Aysegul Dundar",
"Guilin Liu",
"Andrew Tao",
"Bryan Catanzaro"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bhattad_View_Generalization_for_Single_Image_Textured_3D_Models_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bhattad_View_Generalization_for_Single_Image_Textured_3D_Models_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bhattad_View_Generalization_for_CVPR_2021_supplemental.pdf | 2106.06533 | cvf | @InProceedings{Bhattad_2021_CVPR,
author = {Bhattad, Anand and Dundar, Aysegul and Liu, Guilin and Tao, Andrew and Catanzaro, Bryan},
title = {View Generalization for Single Image Textured 3D Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view generalization problems -- the models inferred tend to make poor predictions of appearance in novel views. As for generalization problems in mac... |
Chang_Your_Flamingo_is_My_Bird_Fine-Grained_or_Not_CVPR_2021_paper | Your "Flamingo" is My "Bird": Fine-Grained, or Not | [
"Dongliang Chang",
"Kaiyue Pang",
"Yixiao Zheng",
"Zhanyu Ma",
"Yi-Zhe Song",
"Jun Guo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chang_Your_Flamingo_is_My_Bird_Fine-Grained_or_Not_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chang_Your_Flamingo_is_My_Bird_Fine-Grained_or_Not_CVPR_2021_paper.pdf | null | 2011.09040 | cvf | @InProceedings{Chang_2021_CVPR,
author = {Chang, Dongliang and Pang, Kaiyue and Zheng, Yixiao and Ma, Zhanyu and Song, Yi-Zhe and Guo, Jun},
title = {Your ''Flamingo'' is My ''Bird'': Fine-Grained, or Not},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the majority of us non-experts just "bird" would probably suffice. The real question is therefore -- how can we tailor for different fi... |
Li_Anchor-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2021_paper | Anchor-Constrained Viterbi for Set-Supervised Action Segmentation | [
"Jun Li",
"Sinisa Todorovic"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Anchor-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Anchor-Constrained_Viterbi_for_Set-Supervised_Action_Segmentation_CVPR_2021_paper.pdf | null | 2104.02113 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Jun and Todorovic, Sinisa},
title = {Anchor-Constrained Viterbi for Set-Supervised Action Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video. We use a Hidden Markov Model (HMM) grounded on a multilayer perceptron (MLP) to label video frames, and ... |
Zhu_SOON_Scenario_Oriented_Object_Navigation_With_Graph-Based_Exploration_CVPR_2021_paper | SOON: Scenario Oriented Object Navigation With Graph-Based Exploration | [
"Fengda Zhu",
"Xiwen Liang",
"Yi Zhu",
"Qizhi Yu",
"Xiaojun Chang",
"Xiaodan Liang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_SOON_Scenario_Oriented_Object_Navigation_With_Graph-Based_Exploration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_SOON_Scenario_Oriented_Object_Navigation_With_Graph-Based_Exploration_CVPR_2021_paper.pdf | null | 2103.17138 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Fengda and Liang, Xiwen and Zhu, Yi and Yu, Qizhi and Chang, Xiaojun and Liang, Xiaodan},
title = {SOON: Scenario Oriented Object Navigation With Graph-Based Exploration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | The ability to navigate like a human towards a language-guided target from anywhere in a 3D embodied environment is one of the 'holy grail' goals of intelligent robots. Most visual navigation benchmarks, however, focus on navigating toward a target from a fixed starting point, guided by an elaborate set of instructions... |
Bai_Learning_Scalable_lY-Constrained_Near-Lossless_Image_Compression_via_Joint_Lossy_Image_CVPR_2021_paper | Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression | [
"Yuanchao Bai",
"Xianming Liu",
"Wangmeng Zuo",
"Yaowei Wang",
"Xiangyang Ji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_Learning_Scalable_lY-Constrained_Near-Lossless_Image_Compression_via_Joint_Lossy_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Learning_Scalable_lY-Constrained_Near-Lossless_Image_Compression_via_Joint_Lossy_Image_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_Learning_Scalable_lY-Constrained_CVPR_2021_supplemental.pdf | 2103.17015 | title_judge | @InProceedings{Bai_2021_CVPR,
author = {Bai, Yuanchao and Liu, Xianming and Zuo, Wangmeng and Wang, Yaowei and Ji, Xiangyang},
title = {Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | We propose a novel joint lossy image and residual compression framework for learning l_infinity-constrained near-lossless image compression. Specifically, we obtain a lossy reconstruction of the raw image through lossy image compression and uniformly quantize the corresponding residual to satisfy a given tight l_infini... |
Yu_Minimally_Invasive_Surgery_for_Sparse_Neural_Networks_in_Contrastive_Manner_CVPR_2021_paper | Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner | [
"Chong Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Minimally_Invasive_Surgery_for_Sparse_Neural_Networks_in_Contrastive_Manner_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Minimally_Invasive_Surgery_for_Sparse_Neural_Networks_in_Contrastive_Manner_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Minimally_Invasive_Surgery_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Yu_2021_CVPR,
author = {Yu, Chong},
title = {Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | With the development of deep learning, neural networks tend to be deeper and larger to achieve good performance. Trained models are more compute-intensive and memory-intensive, which lead to the big challenges on memory bandwidth, storage, latency, and throughput. In this paper, we propose the neural network compressio... |
Kim_XProtoNet_Diagnosis_in_Chest_Radiography_With_Global_and_Local_Explanations_CVPR_2021_paper | XProtoNet: Diagnosis in Chest Radiography With Global and Local Explanations | [
"Eunji Kim",
"Siwon Kim",
"Minji Seo",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_XProtoNet_Diagnosis_in_Chest_Radiography_With_Global_and_Local_Explanations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_XProtoNet_Diagnosis_in_Chest_Radiography_With_Global_and_Local_Explanations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_XProtoNet_Diagnosis_in_CVPR_2021_supplemental.pdf | 2103.10663 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Eunji and Kim, Siwon and Seo, Minji and Yoon, Sungroh},
title = {XProtoNet: Diagnosis in Chest Radiography With Global and Local Explanations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases. However, existing methods only provide predictions without accurate explanations, undermining the trustworthiness of the diagnostic methods. Here, we present XProtoNet, a globally and locally inte... |
Sun_Learning_Scene_Structure_Guidance_via_Cross-Task_Knowledge_Transfer_for_Single_CVPR_2021_paper | Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution | [
"Baoli Sun",
"Xinchen Ye",
"Baopu Li",
"Haojie Li",
"Zhihui Wang",
"Rui Xu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Learning_Scene_Structure_Guidance_via_Cross-Task_Knowledge_Transfer_for_Single_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Learning_Scene_Structure_Guidance_via_Cross-Task_Knowledge_Transfer_for_Single_CVPR_2021_paper.pdf | null | 2103.12955 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Baoli and Ye, Xinchen and Li, Baopu and Li, Haojie and Wang, Zhihui and Xu, Rui},
title = {Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | Existing color-guided depth super-resolution (DSR) approaches require paired RGB-D data as training examples where the RGB image is used as structural guidance to recover the degraded depth map due to their geometrical similarity. However, the paired data may be limited or expensive to be collected in actual testing en... |
Mayo_Visual_Navigation_With_Spatial_Attention_CVPR_2021_paper | Visual Navigation With Spatial Attention | [
"Bar Mayo",
"Tamir Hazan",
"Ayellet Tal"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mayo_Visual_Navigation_With_Spatial_Attention_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mayo_Visual_Navigation_With_Spatial_Attention_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mayo_Visual_Navigation_With_CVPR_2021_supplemental.pdf | 2104.09807 | cvf | @InProceedings{Mayo_2021_CVPR,
author = {Mayo, Bar and Hazan, Tamir and Tal, Ayellet},
title = {Visual Navigation With Spatial Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | This work focuses on object goal visual navigation, aiming at finding the location of an object from a given class, where in each step the agent is provided with an egocentric RGB image of the scene. We propose to learn the agent's policy using a reinforcement learning algorithm. Our key contribution is a novel attenti... |
Chen_Model-Based_3D_Hand_Reconstruction_via_Self-Supervised_Learning_CVPR_2021_paper | Model-Based 3D Hand Reconstruction via Self-Supervised Learning | [
"Yujin Chen",
"Zhigang Tu",
"Di Kang",
"Linchao Bao",
"Ying Zhang",
"Xuefei Zhe",
"Ruizhi Chen",
"Junsong Yuan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Model-Based_3D_Hand_Reconstruction_via_Self-Supervised_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Model-Based_3D_Hand_Reconstruction_via_Self-Supervised_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Model-Based_3D_Hand_CVPR_2021_supplemental.pdf | 2103.11703 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Yujin and Tu, Zhigang and Kang, Di and Bao, Linchao and Zhang, Ying and Zhe, Xuefei and Chen, Ruizhi and Yuan, Junsong},
title = {Model-Based 3D Hand Reconstruction via Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | Reconstructing a 3D hand from a single-view RGB image is challenging due to various hand configurations and depth ambiguity. To reliably reconstruct a 3D hand from a monocular image, most state-of-the-art methods heavily rely on 3D annotations at the training stage, but obtaining 3D annotations is expensive. To allevia... |
Lei_Robust_Reflection_Removal_With_Reflection-Free_Flash-Only_Cues_CVPR_2021_paper | Robust Reflection Removal With Reflection-Free Flash-Only Cues | [
"Chenyang Lei",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lei_Robust_Reflection_Removal_With_Reflection-Free_Flash-Only_Cues_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lei_Robust_Reflection_Removal_With_Reflection-Free_Flash-Only_Cues_CVPR_2021_paper.pdf | null | 2103.04273 | cvf | @InProceedings{Lei_2021_CVPR,
author = {Lei, Chenyang and Chen, Qifeng},
title = {Robust Reflection Removal With Reflection-Free Flash-Only Cues},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent t... |
Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper | Real-Time Selfie Video Stabilization | [
"Jiyang Yu",
"Ravi Ramamoorthi",
"Keli Cheng",
"Michel Sarkis",
"Ning Bi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Real-Time_Selfie_Video_CVPR_2021_supplemental.zip | 2009.02007 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Jiyang and Ramamoorthi, Ravi and Cheng, Keli and Sarkis, Michel and Bi, Ning},
title = {Real-Time Selfie Video Stabilization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is s... |
Li_3D_Human_Action_Representation_Learning_via_Cross-View_Consistency_Pursuit_CVPR_2021_paper | 3D Human Action Representation Learning via Cross-View Consistency Pursuit | [
"Linguo Li",
"Minsi Wang",
"Bingbing Ni",
"Hang Wang",
"Jiancheng Yang",
"Wenjun Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_3D_Human_Action_Representation_Learning_via_Cross-View_Consistency_Pursuit_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_3D_Human_Action_Representation_Learning_via_Cross-View_Consistency_Pursuit_CVPR_2021_paper.pdf | null | 2104.14466 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Linguo and Wang, Minsi and Ni, Bingbing and Wang, Hang and Yang, Jiancheng and Zhang, Wenjun},
title = {3D Human Action Representation Learning via Cross-View Consistency Pursuit},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action representation (CrosSCLR), by leveraging multi-view complementary supervision signal. CrosSCLR consists of both single-view contrastive learning (SkeletonCLR) and cross-view consistent knowledge mining (CVC-KM... |
Karkus_Differentiable_SLAM-Net_Learning_Particle_SLAM_for_Visual_Navigation_CVPR_2021_paper | Differentiable SLAM-Net: Learning Particle SLAM for Visual Navigation | [
"Peter Karkus",
"Shaojun Cai",
"David Hsu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Karkus_Differentiable_SLAM-Net_Learning_Particle_SLAM_for_Visual_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Karkus_Differentiable_SLAM-Net_Learning_Particle_SLAM_for_Visual_Navigation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Karkus_Differentiable_SLAM-Net_Learning_CVPR_2021_supplemental.pdf | 2105.07593 | title_snapshot | @InProceedings{Karkus_2021_CVPR,
author = {Karkus, Peter and Cai, Shaojun and Hsu, David},
title = {Differentiable SLAM-Net: Learning Particle SLAM for Visual Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar rob... |
Epstein_Learning_Goals_From_Failure_CVPR_2021_paper | Learning Goals From Failure | [
"Dave Epstein",
"Carl Vondrick"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Epstein_Learning_Goals_From_Failure_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Epstein_Learning_Goals_From_Failure_CVPR_2021_paper.pdf | null | 2006.15657 | cvf | @InProceedings{Epstein_2021_CVPR,
author = {Epstein, Dave and Vondrick, Carl},
title = {Learning Goals From Failure},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {11194-11204}
} | We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent ... |
Liu_Rank-One_Prior_Toward_Real-Time_Scene_Recovery_CVPR_2021_paper | Rank-One Prior: Toward Real-Time Scene Recovery | [
"Jun Liu",
"Wen Liu",
"Jianing Sun",
"Tieyong Zeng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Rank-One_Prior_Toward_Real-Time_Scene_Recovery_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Rank-One_Prior_Toward_Real-Time_Scene_Recovery_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Rank-One_Prior_Toward_CVPR_2021_supplemental.pdf | 2103.17126 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Jun and Liu, Wen and Sun, Jianing and Zeng, Tieyong},
title = {Rank-One Prior: Toward Real-Time Scene Recovery},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year =... | Scene recovery is a fundamental imaging task for several practical applications, e.g., video surveillance and autonomous vehicles, etc. To improve visual quality under different weather/imaging conditions, we propose a real-time light correction method to recover the degraded scenes in the cases of sandstorms, underwat... |
Ng_Body2Hands_Learning_To_Infer_3D_Hands_From_Conversational_Gesture_Body_CVPR_2021_paper | Body2Hands: Learning To Infer 3D Hands From Conversational Gesture Body Dynamics | [
"Evonne Ng",
"Shiry Ginosar",
"Trevor Darrell",
"Hanbyul Joo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ng_Body2Hands_Learning_To_Infer_3D_Hands_From_Conversational_Gesture_Body_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ng_Body2Hands_Learning_To_Infer_3D_Hands_From_Conversational_Gesture_Body_CVPR_2021_paper.pdf | null | 2007.12287 | cvf | @InProceedings{Ng_2021_CVPR,
author = {Ng, Evonne and Ginosar, Shiry and Darrell, Trevor and Joo, Hanbyul},
title = {Body2Hands: Learning To Infer 3D Hands From Conversational Gesture Body Dynamics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | We propose a novel learned deep prior of body motion for 3D hand shape synthesis and estimation in the domain of conversational gestures. Our model builds upon the insight that body motion and hand gestures are strongly correlated in non-verbal communication settings. We formulate the learning of this prior as a predic... |
Xu_Linear_Semantics_in_Generative_Adversarial_Networks_CVPR_2021_paper | Linear Semantics in Generative Adversarial Networks | [
"Jianjin Xu",
"Changxi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Linear_Semantics_in_Generative_Adversarial_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Linear_Semantics_in_Generative_Adversarial_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_Linear_Semantics_in_CVPR_2021_supplemental.pdf | 2104.00487 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Jianjin and Zheng, Changxi},
title = {Linear Semantics in Generative Adversarial Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages ... | Generative Adversarial Networks (GANs) are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. In this work, we aim to better understand the semantic representation of GANs, and thereby enable semantic control in GAN's generation process. Interesting... |
Zhou_Mesoscopic_Photogrammetry_With_an_Unstabilized_Phone_Camera_CVPR_2021_paper | Mesoscopic Photogrammetry With an Unstabilized Phone Camera | [
"Kevin C. Zhou",
"Colin Cooke",
"Jaehee Park",
"Ruobing Qian",
"Roarke Horstmeyer",
"Joseph A. Izatt",
"Sina Farsiu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhou_Mesoscopic_Photogrammetry_With_an_Unstabilized_Phone_Camera_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Mesoscopic_Photogrammetry_With_an_Unstabilized_Phone_Camera_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhou_Mesoscopic_Photogrammetry_With_CVPR_2021_supplemental.pdf | 2012.06044 | cvf | @InProceedings{Zhou_2021_CVPR,
author = {Zhou, Kevin C. and Cooke, Colin and Park, Jaehee and Qian, Ruobing and Horstmeyer, Roarke and Izatt, Joseph A. and Farsiu, Sina},
title = {Mesoscopic Photogrammetry With an Unstabilized Phone Camera},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-ba... |
Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper | Joint Generative and Contrastive Learning for Unsupervised Person Re-Identification | [
"Hao Chen",
"Yaohui Wang",
"Benoit Lagadec",
"Antitza Dantcheva",
"Francois Bremond"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Joint_Generative_and_Contrastive_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Joint_Generative_and_CVPR_2021_supplemental.pdf | 2012.09071 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Hao and Wang, Yaohui and Lagadec, Benoit and Dantcheva, Antitza and Bremond, Francois},
title = {Joint Generative and Contrastive Learning for Unsupervised Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one join... |
Xu_Wide-Baseline_Multi-Camera_Calibration_Using_Person_Re-Identification_CVPR_2021_paper | Wide-Baseline Multi-Camera Calibration Using Person Re-Identification | [
"Yan Xu",
"Yu-Jhe Li",
"Xinshuo Weng",
"Kris Kitani"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_Wide-Baseline_Multi-Camera_Calibration_Using_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_Wide-Baseline_Multi-Camera_Calibration_Using_Person_Re-Identification_CVPR_2021_paper.pdf | null | 2104.08568 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Yan and Li, Yu-Jhe and Weng, Xinshuo and Kitani, Kris},
title = {Wide-Baseline Multi-Camera Calibration Using Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. This task is challenging since detecting and matching the same 3D keypoint observed from two very different camera views is di... |
Huo_ATSO_Asynchronous_Teacher-Student_Optimization_for_Semi-Supervised_Image_Segmentation_CVPR_2021_paper | ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation | [
"Xinyue Huo",
"Lingxi Xie",
"Jianzhong He",
"Zijie Yang",
"Wengang Zhou",
"Houqiang Li",
"Qi Tian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Huo_ATSO_Asynchronous_Teacher-Student_Optimization_for_Semi-Supervised_Image_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Huo_ATSO_Asynchronous_Teacher-Student_Optimization_for_Semi-Supervised_Image_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Huo_ATSO_Asynchronous_Teacher-Student_CVPR_2021_supplemental.pdf | 2006.13461 | title_judge | @InProceedings{Huo_2021_CVPR,
author = {Huo, Xinyue and Xie, Lingxi and He, Jianzhong and Yang, Zijie and Zhou, Wengang and Li, Houqiang and Tian, Qi},
title = {ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference... | Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data. This paper focuses on a popular pipeline known as self-learning, where we point out a weakness named lazy mimicking that refers to the inertia tha... |
Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper | Panoramic Image Reflection Removal | [
"Yuchen Hong",
"Qian Zheng",
"Lingran Zhao",
"Xudong Jiang",
"Alex C. Kot",
"Boxin Shi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hong_Panoramic_Image_Reflection_CVPR_2021_supplemental.zip | null | null | @InProceedings{Hong_2021_CVPR,
author = {Hong, Yuchen and Zheng, Qian and Zhao, Lingran and Jiang, Xudong and Kot, Alex C. and Shi, Boxin},
title = {Panoramic Image Reflection Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contam... |
Tan_OTCE_A_Transferability_Metric_for_Cross-Domain_Cross-Task_Representations_CVPR_2021_paper | OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations | [
"Yang Tan",
"Yang Li",
"Shao-Lun Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_OTCE_A_Transferability_Metric_for_Cross-Domain_Cross-Task_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_OTCE_A_Transferability_Metric_for_Cross-Domain_Cross-Task_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_OTCE_A_Transferability_CVPR_2021_supplemental.pdf | 2103.13843 | cvf | @InProceedings{Tan_2021_CVPR,
author = {Tan, Yang and Li, Yang and Huang, Shao-Lun},
title = {OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Transfer learning across heterogeneous data distributions (a.k.a. domains) and distinct tasks is a more general and challenging problem than conventional transfer learning, where either domains or tasks are assumed to be the same. While neural network based feature transfer is widely used in transfer learning applicati... |
Tan_Diverse_Semantic_Image_Synthesis_via_Probability_Distribution_Modeling_CVPR_2021_paper | Diverse Semantic Image Synthesis via Probability Distribution Modeling | [
"Zhentao Tan",
"Menglei Chai",
"Dongdong Chen",
"Jing Liao",
"Qi Chu",
"Bin Liu",
"Gang Hua",
"Nenghai Yu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tan_Diverse_Semantic_Image_Synthesis_via_Probability_Distribution_Modeling_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tan_Diverse_Semantic_Image_Synthesis_via_Probability_Distribution_Modeling_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tan_Diverse_Semantic_Image_CVPR_2021_supplemental.pdf | 2103.06878 | cvf | @InProceedings{Tan_2021_CVPR,
author = {Tan, Zhentao and Chai, Menglei and Chen, Dongdong and Liao, Jing and Chu, Qi and Liu, Bin and Hua, Gang and Yu, Nenghai},
title = {Diverse Semantic Image Synthesis via Probability Distribution Modeling},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | Semantic image synthesis, translating semantic layouts to photo-realistic images, is a one-to-many mapping problem. Though impressive progress has been recently made, diverse semantic synthesis that can efficiently produce semantic-level multimodal results, still remains a challenge. In this paper, we propose a novel d... |
Martin-Brualla_NeRF_in_the_Wild_Neural_Radiance_Fields_for_Unconstrained_Photo_CVPR_2021_paper | NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections | [
"Ricardo Martin-Brualla",
"Noha Radwan",
"Mehdi S. M. Sajjadi",
"Jonathan T. Barron",
"Alexey Dosovitskiy",
"Daniel Duckworth"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Martin-Brualla_NeRF_in_the_Wild_Neural_Radiance_Fields_for_Unconstrained_Photo_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Martin-Brualla_NeRF_in_the_Wild_Neural_Radiance_Fields_for_Unconstrained_Photo_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Martin-Brualla_NeRF_in_the_CVPR_2021_supplemental.pdf | 2008.02268 | title_snapshot | @InProceedings{Martin-Brualla_2021_CVPR,
author = {Martin-Brualla, Ricardo and Radwan, Noha and Sajjadi, Mehdi S. M. and Barron, Jonathan T. and Dosovitskiy, Alexey and Duckworth, Daniel},
title = {NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections},
booktitle = {Proceedings... | We present a learning-based method for synthesizingnovel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the density and color of a scene as a function of 3D coordinates. While ... |
Zhang_Learning_by_Watching_CVPR_2021_paper | Learning by Watching | [
"Jimuyang Zhang",
"Eshed Ohn-Bar"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Learning_by_Watching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Learning_by_Watching_CVPR_2021_paper.pdf | null | 2106.05966 | title_snapshot | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Jimuyang and Ohn-Bar, Eshed},
title = {Learning by Watching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {12711-12721}
} | When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle ... |
Wang_Pseudo_Facial_Generation_With_Extreme_Poses_for_Face_Recognition_CVPR_2021_paper | Pseudo Facial Generation With Extreme Poses for Face Recognition | [
"Guoli Wang",
"Jiaqi Ma",
"Qian Zhang",
"Jiwen Lu",
"Jie Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Pseudo_Facial_Generation_With_Extreme_Poses_for_Face_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Pseudo_Facial_Generation_With_Extreme_Poses_for_Face_Recognition_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Guoli and Ma, Jiaqi and Zhang, Qian and Lu, Jiwen and Zhou, Jie},
title = {Pseudo Facial Generation With Extreme Poses for Face Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity ... |
Piao_Inverting_Generative_Adversarial_Renderer_for_Face_Reconstruction_CVPR_2021_paper | Inverting Generative Adversarial Renderer for Face Reconstruction | [
"Jingtan Piao",
"Keqiang Sun",
"Quan Wang",
"Kwan-Yee Lin",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Piao_Inverting_Generative_Adversarial_Renderer_for_Face_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Piao_Inverting_Generative_Adversarial_Renderer_for_Face_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Piao_Inverting_Generative_Adversarial_CVPR_2021_supplemental.pdf | 2105.02431 | cvf | @InProceedings{Piao_2021_CVPR,
author = {Piao, Jingtan and Sun, Keqiang and Wang, Quan and Lin, Kwan-Yee and Li, Hongsheng},
title = {Inverting Generative Adversarial Renderer for Face Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Given a monocular face image as input, 3D face geometry reconstruction aims to recover a corresponding 3Dface mesh. Recently, both optimization-based and learning-based face reconstruction methods have taken advantage of the emerging differentiable renderer and shown promising results. However, the differentiable rende... |
Chen_Efficient_Object_Embedding_for_Spliced_Image_Retrieval_CVPR_2021_paper | Efficient Object Embedding for Spliced Image Retrieval | [
"Bor-Chun Chen",
"Zuxuan Wu",
"Larry S. Davis",
"Ser-Nam Lim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Efficient_Object_Embedding_for_Spliced_Image_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Efficient_Object_Embedding_for_Spliced_Image_Retrieval_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Efficient_Object_Embedding_CVPR_2021_supplemental.pdf | 1905.11903 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Bor-Chun and Wu, Zuxuan and Davis, Larry S. and Lim, Ser-Nam},
title = {Efficient Object Embedding for Spliced Image Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Detecting spliced images is one of the emerging challenges in computer vision. Unlike prior methods that focus on detecting low-level artifacts generated during the manipulation process, we use an image retrieval approach to tackle this problem. When given a spliced query image, our goal is to retrieve the original ima... |
Kumar_GrooMeD-NMS_Grouped_Mathematically_Differentiable_NMS_for_Monocular_3D_Object_Detection_CVPR_2021_paper | GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection | [
"Abhinav Kumar",
"Garrick Brazil",
"Xiaoming Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kumar_GrooMeD-NMS_Grouped_Mathematically_Differentiable_NMS_for_Monocular_3D_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kumar_GrooMeD-NMS_Grouped_Mathematically_Differentiable_NMS_for_Monocular_3D_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kumar_GrooMeD-NMS_Grouped_Mathematically_CVPR_2021_supplemental.zip | 2103.17202 | title_snapshot | @InProceedings{Kumar_2021_CVPR,
author = {Kumar, Abhinav and Brazil, Garrick and Liu, Xiaoming},
title = {GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Modern 3D object detectors have immensely benefited from the end-to-end learning idea. However, most of them use a post-processing algorithm called Non-Maximal Suppression (NMS) only during inference. While there were attempts to include NMS in the training pipeline for tasks such as 2D object detection, they have been... |
Ren_Flow_Guided_Transformable_Bottleneck_Networks_for_Motion_Retargeting_CVPR_2021_paper | Flow Guided Transformable Bottleneck Networks for Motion Retargeting | [
"Jian Ren",
"Menglei Chai",
"Oliver J. Woodford",
"Kyle Olszewski",
"Sergey Tulyakov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ren_Flow_Guided_Transformable_Bottleneck_Networks_for_Motion_Retargeting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ren_Flow_Guided_Transformable_Bottleneck_Networks_for_Motion_Retargeting_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ren_Flow_Guided_Transformable_CVPR_2021_supplemental.zip | 2106.07771 | cvf | @InProceedings{Ren_2021_CVPR,
author = {Ren, Jian and Chai, Menglei and Woodford, Oliver J. and Olszewski, Kyle and Tulyakov, Sergey},
title = {Flow Guided Transformable Bottleneck Networks for Motion Retargeting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Human motion retargeting aims to transfer the motion of one person in a driving video or set of images to another person. Existing efforts leverage a long training video from each target person to train a subject-specific motion transfer model. However, the scalability of such methods is limited, as each model can only... |
Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper | Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation | [
"Weixiang Yang",
"Qi Li",
"Wenxi Liu",
"Yuanlong Yu",
"Yuexin Ma",
"Shengfeng He",
"Jia Pan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Projecting_Your_View_CVPR_2021_supplemental.zip | null | null | @InProceedings{Yang_2021_CVPR,
author = {Yang, Weixiang and Li, Qi and Liu, Wenxi and Yu, Yuanlong and Ma, Yuexin and He, Shengfeng and Pan, Jia},
title = {Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation},
booktitle = {Proceedings of the IEEE/CVF... | HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to the deployed expensive sensors and time-consuming computation. Camera-based methods usually need to separately perform road segmentation and view transformation, which often causes distortion and the absence of content. To p... |
Chen_Deep_Analysis_of_CNN-Based_Spatio-Temporal_Representations_for_Action_Recognition_CVPR_2021_paper | Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition | [
"Chun-Fu Richard Chen",
"Rameswar Panda",
"Kandan Ramakrishnan",
"Rogerio Feris",
"John Cohn",
"Aude Oliva",
"Quanfu Fan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Deep_Analysis_of_CNN-Based_Spatio-Temporal_Representations_for_Action_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Deep_Analysis_of_CNN-Based_Spatio-Temporal_Representations_for_Action_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Deep_Analysis_of_CVPR_2021_supplemental.pdf | 2010.11757 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Chun-Fu Richard and Panda, Rameswar and Ramakrishnan, Kandan and Feris, Rogerio and Cohn, John and Oliva, Aude and Fan, Quanfu},
title = {Deep Analysis of CNN-Based Spatio-Temporal Representations for Action Recognition},
booktitle = {Proceedings of the ... | In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets. In this paper, we carry out in-depth comparative analysis to better understand the differences between th... |
Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper | Generalizable Person Re-Identification With Relevance-Aware Mixture of Experts | [
"Yongxing Dai",
"Xiaotong Li",
"Jun Liu",
"Zekun Tong",
"Ling-Yu Duan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper.pdf | null | 2105.09156 | cvf | @InProceedings{Dai_2021_CVPR,
author = {Dai, Yongxing and Li, Xiaotong and Liu, Jun and Tong, Zekun and Duan, Ling-Yu},
title = {Generalizable Person Re-Identification With Relevance-Aware Mixture of Experts},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit... | Domain generalizable (DG) person re-identification (ReID) is a challenging problem because we cannot access any unseen target domain data during training. Almost all the existing DG ReID methods follow the same pipeline where they use a hybrid dataset from multiple source domains for training, and then directly apply t... |
de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_paper | Part-Aware Panoptic Segmentation | [
"Daan de Geus",
"Panagiotis Meletis",
"Chenyang Lu",
"Xiaoxiao Wen",
"Gijs Dubbelman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/de_Geus_Part-Aware_Panoptic_Segmentation_CVPR_2021_supplemental.pdf | 2106.06351 | cvf | @InProceedings{de_Geus_2021_CVPR,
author = {de Geus, Daan and Meletis, Panagiotis and Lu, Chenyang and Wen, Xiaoxiao and Dubbelman, Gijs},
title = {Part-Aware Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Citysc... |
Wang_Unsupervised_Degradation_Representation_Learning_for_Blind_Super-Resolution_CVPR_2021_paper | Unsupervised Degradation Representation Learning for Blind Super-Resolution | [
"Longguang Wang",
"Yingqian Wang",
"Xiaoyu Dong",
"Qingyu Xu",
"Jungang Yang",
"Wei An",
"Yulan Guo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Unsupervised_Degradation_Representation_Learning_for_Blind_Super-Resolution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Unsupervised_Degradation_Representation_Learning_for_Blind_Super-Resolution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Unsupervised_Degradation_Representation_CVPR_2021_supplemental.pdf | 2104.00416 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Longguang and Wang, Yingqian and Dong, Xiaoyu and Xu, Qingyu and Yang, Jungang and An, Wei and Guo, Yulan},
title = {Unsupervised Degradation Representation Learning for Blind Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Comp... | Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these methods suffer a severe performance drop when the real degradation is different from their assumption. To handle various unknown degradations in... |
Min_Convolutional_Hough_Matching_Networks_CVPR_2021_paper | Convolutional Hough Matching Networks | [
"Juhong Min",
"Minsu Cho"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Min_Convolutional_Hough_Matching_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Min_Convolutional_Hough_Matching_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Min_Convolutional_Hough_Matching_CVPR_2021_supplemental.pdf | 2103.16831 | cvf | @InProceedings{Min_2021_CVPR,
author = {Min, Juhong and Cho, Minsu},
title = {Convolutional Hough Matching Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {2940-2950}
} | Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolu... |
Ye_Hierarchical_and_Partially_Observable_Goal-Driven_Policy_Learning_With_Goals_Relational_CVPR_2021_paper | Hierarchical and Partially Observable Goal-Driven Policy Learning With Goals Relational Graph | [
"Xin Ye",
"Yezhou Yang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ye_Hierarchical_and_Partially_Observable_Goal-Driven_Policy_Learning_With_Goals_Relational_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ye_Hierarchical_and_Partially_Observable_Goal-Driven_Policy_Learning_With_Goals_Relational_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ye_Hierarchical_and_Partially_CVPR_2021_supplemental.pdf | 2103.01350 | cvf | @InProceedings{Ye_2021_CVPR,
author = {Ye, Xin and Yang, Yezhou},
title = {Hierarchical and Partially Observable Goal-Driven Policy Learning With Goals Relational Graph},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical p... |
Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper | Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos | [
"Hehe Fan",
"Yi Yang",
"Mohan Kankanhalli"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fan_Point_4D_Transformer_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Fan_2021_CVPR,
author = {Fan, Hehe and Yang, Yi and Kankanhalli, Mohan},
title = {Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Point cloud videos exhibit irregularities and lack of order along the spatial dimension where points emerge inconsistently across different frames. To capture the dynamics in point cloud videos, point tracking is usually employed. However, as points may flow in and out across frames, computing accurate point trajectori... |
Lal_CoCoNets_Continuous_Contrastive_3D_Scene_Representations_CVPR_2021_paper | CoCoNets: Continuous Contrastive 3D Scene Representations | [
"Shamit Lal",
"Mihir Prabhudesai",
"Ishita Mediratta",
"Adam W. Harley",
"Katerina Fragkiadaki"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lal_CoCoNets_Continuous_Contrastive_3D_Scene_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lal_CoCoNets_Continuous_Contrastive_3D_Scene_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lal_CoCoNets_Continuous_Contrastive_CVPR_2021_supplemental.zip | 2104.03851 | cvf | @InProceedings{Lal_2021_CVPR,
author = {Lal, Shamit and Prabhudesai, Mihir and Mediratta, Ishita and Harley, Adam W. and Fragkiadaki, Katerina},
title = {CoCoNets: Continuous Contrastive 3D Scene Representations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection. The mode... |
Zhang_Distribution_Alignment_A_Unified_Framework_for_Long-Tail_Visual_Recognition_CVPR_2021_paper | Distribution Alignment: A Unified Framework for Long-Tail Visual Recognition | [
"Songyang Zhang",
"Zeming Li",
"Shipeng Yan",
"Xuming He",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Distribution_Alignment_A_Unified_Framework_for_Long-Tail_Visual_Recognition_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Distribution_Alignment_A_Unified_Framework_for_Long-Tail_Visual_Recognition_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Distribution_Alignment_A_CVPR_2021_supplemental.pdf | 2103.16370 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Songyang and Li, Zeming and Yan, Shipeng and He, Xuming and Sun, Jian},
title = {Distribution Alignment: A Unified Framework for Long-Tail Visual Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Despite the success of the deep neural networks, it remains challenging to effectively build a system for long-tail visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we develop a unified ... |
Li_Dynamic_Class_Queue_for_Large_Scale_Face_Recognition_in_the_CVPR_2021_paper | Dynamic Class Queue for Large Scale Face Recognition in the Wild | [
"Bi Li",
"Teng Xi",
"Gang Zhang",
"Haocheng Feng",
"Junyu Han",
"Jingtuo Liu",
"Errui Ding",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Class_Queue_for_Large_Scale_Face_Recognition_in_the_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Class_Queue_for_Large_Scale_Face_Recognition_in_the_CVPR_2021_paper.pdf | null | 2105.11113 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Bi and Xi, Teng and Zhang, Gang and Feng, Haocheng and Han, Junyu and Liu, Jingtuo and Ding, Errui and Liu, Wenyu},
title = {Dynamic Class Queue for Large Scale Face Recognition in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on computing resource constraint and long-tailed class distribution. Recently, classification-based representati... |
Yang_3D-MAN_3D_Multi-Frame_Attention_Network_for_Object_Detection_CVPR_2021_paper | 3D-MAN: 3D Multi-Frame Attention Network for Object Detection | [
"Zetong Yang",
"Yin Zhou",
"Zhifeng Chen",
"Jiquan Ngiam"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_3D-MAN_3D_Multi-Frame_Attention_Network_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_3D-MAN_3D_Multi-Frame_Attention_Network_for_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_3D-MAN_3D_Multi-Frame_CVPR_2021_supplemental.pdf | 2103.16054 | title_snapshot | @InProceedings{Yang_2021_CVPR,
author = {Yang, Zetong and Zhou, Yin and Chen, Zhifeng and Ngiam, Jiquan},
title = {3D-MAN: 3D Multi-Frame Attention Network for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | 3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully utilize information from multiple frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates ... |
Jing_Cross-Modal_Center_Loss_for_3D_Cross-Modal_Retrieval_CVPR_2021_paper | Cross-Modal Center Loss for 3D Cross-Modal Retrieval | [
"Longlong Jing",
"Elahe Vahdani",
"Jiaxing Tan",
"Yingli Tian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jing_Cross-Modal_Center_Loss_for_3D_Cross-Modal_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Cross-Modal_Center_Loss_for_3D_Cross-Modal_Retrieval_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Jing_2021_CVPR,
author = {Jing, Longlong and Vahdani, Elahe and Tan, Jiaxing and Tian, Yingli},
title = {Cross-Modal Center Loss for 3D Cross-Modal Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we propose an approach to jointly train the components of cross-modal retrieval framework wi... |
Sun_Learning_View_Selection_for_3D_Scenes_CVPR_2021_paper | Learning View Selection for 3D Scenes | [
"Yifan Sun",
"Qixing Huang",
"Dun-Yu Hsiao",
"Li Guan",
"Gang Hua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_Learning_View_Selection_for_3D_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Learning_View_Selection_for_3D_Scenes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_Learning_View_Selection_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Sun_2021_CVPR,
author = {Sun, Yifan and Huang, Qixing and Hsiao, Dun-Yu and Guan, Li and Hua, Gang},
title = {Learning View Selection for 3D Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond. A standard approach is to explicitly sample a densecollection of views and formulate it as a view selection prob-lem, or, more generally, a set cover problem. In this paper,we introduce a novel appro... |
Wang_FESTA_Flow_Estimation_via_Spatial-Temporal_Attention_for_Scene_Point_Clouds_CVPR_2021_paper | FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds | [
"Haiyan Wang",
"Jiahao Pang",
"Muhammad A. Lodhi",
"Yingli Tian",
"Dong Tian"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_FESTA_Flow_Estimation_via_Spatial-Temporal_Attention_for_Scene_Point_Clouds_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_FESTA_Flow_Estimation_via_Spatial-Temporal_Attention_for_Scene_Point_Clouds_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_FESTA_Flow_Estimation_CVPR_2021_supplemental.pdf | 2104.00798 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Haiyan and Pang, Jiahao and Lodhi, Muhammad A. and Tian, Yingli and Tian, Dong},
title = {FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via p... |
Singh_Semi-Supervised_Action_Recognition_With_Temporal_Contrastive_Learning_CVPR_2021_paper | Semi-Supervised Action Recognition With Temporal Contrastive Learning | [
"Ankit Singh",
"Omprakash Chakraborty",
"Ashutosh Varshney",
"Rameswar Panda",
"Rogerio Feris",
"Kate Saenko",
"Abir Das"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Singh_Semi-Supervised_Action_Recognition_With_Temporal_Contrastive_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Singh_Semi-Supervised_Action_Recognition_With_Temporal_Contrastive_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Singh_Semi-Supervised_Action_Recognition_CVPR_2021_supplemental.pdf | 2102.02751 | cvf | @InProceedings{Singh_2021_CVPR,
author = {Singh, Ankit and Chakraborty, Omprakash and Varshney, Ashutosh and Panda, Rameswar and Feris, Rogerio and Saenko, Kate and Das, Abir},
title = {Semi-Supervised Action Recognition With Temporal Contrastive Learning},
booktitle = {Proceedings of the IEEE/CVF Co... | Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video sp... |
Liu_SG-Net_Spatial_Granularity_Network_for_One-Stage_Video_Instance_Segmentation_CVPR_2021_paper | SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation | [
"Dongfang Liu",
"Yiming Cui",
"Wenbo Tan",
"Yingjie Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_SG-Net_Spatial_Granularity_Network_for_One-Stage_Video_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_SG-Net_Spatial_Granularity_Network_for_One-Stage_Video_Instance_Segmentation_CVPR_2021_paper.pdf | null | 2103.10284 | title_snapshot | @InProceedings{Liu_2021_CVPR,
author = {Liu, Dongfang and Cui, Yiming and Tan, Wenbo and Chen, Yingjie},
title = {SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granulari... |
Tancik_Learned_Initializations_for_Optimizing_Coordinate-Based_Neural_Representations_CVPR_2021_paper | Learned Initializations for Optimizing Coordinate-Based Neural Representations | [
"Matthew Tancik",
"Ben Mildenhall",
"Terrance Wang",
"Divi Schmidt",
"Pratul P. Srinivasan",
"Jonathan T. Barron",
"Ren Ng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tancik_Learned_Initializations_for_Optimizing_Coordinate-Based_Neural_Representations_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tancik_Learned_Initializations_for_Optimizing_Coordinate-Based_Neural_Representations_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tancik_Learned_Initializations_for_CVPR_2021_supplemental.pdf | 2012.02189 | cvf | @InProceedings{Tancik_2021_CVPR,
author = {Tancik, Matthew and Mildenhall, Ben and Wang, Terrance and Schmidt, Divi and Srinivasan, Pratul P. and Barron, Jonathan T. and Ng, Ren},
title = {Learned Initializations for Optimizing Coordinate-Based Neural Representations},
booktitle = {Proceedings of the... | Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-le... |
Pan_Actor-Context-Actor_Relation_Network_for_Spatio-Temporal_Action_Localization_CVPR_2021_paper | Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization | [
"Junting Pan",
"Siyu Chen",
"Mike Zheng Shou",
"Yu Liu",
"Jing Shao",
"Hongsheng Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Pan_Actor-Context-Actor_Relation_Network_for_Spatio-Temporal_Action_Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Pan_Actor-Context-Actor_Relation_Network_for_Spatio-Temporal_Action_Localization_CVPR_2021_paper.pdf | null | 2006.07976 | cvf | @InProceedings{Pan_2021_CVPR,
author = {Pan, Junting and Chen, Siyu and Shou, Mike Zheng and Liu, Yu and Shao, Jing and Li, Hongsheng},
title = {Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pa... | Localizing persons and recognizing their actions from videos is a challenging task towards high-level video under-standing. Recent advances have been achieved by modeling direct pairwise relations between entities. In this paper, we take one step further, not only model direct relations between pairs but also take into... |
Zhang_Cross-View_Cross-Scene_Multi-View_Crowd_Counting_CVPR_2021_paper | Cross-View Cross-Scene Multi-View Crowd Counting | [
"Qi Zhang",
"Wei Lin",
"Antoni B. Chan"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Cross-View_Cross-Scene_Multi-View_Crowd_Counting_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Cross-View_Cross-Scene_Multi-View_Crowd_Counting_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Cross-View_Cross-Scene_Multi-View_CVPR_2021_supplemental.pdf | 2205.01551 | title_snapshot | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Qi and Lin, Wei and Chan, Antoni B.},
title = {Cross-View Cross-Scene Multi-View Crowd Counting},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Multi-view crowd counting has been previously proposed to utilize multi-cameras to extend the field-of-view of a single camera, capturing more people in the scene, and improve counting performance for occluded people or those in low resolution. However, the current multi-view paradigm trains and tests on the same singl... |
Li_Semantic_Segmentation_With_Generative_Models_Semi-Supervised_Learning_and_Strong_Out-of-Domain_CVPR_2021_paper | Semantic Segmentation With Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization | [
"Daiqing Li",
"Junlin Yang",
"Karsten Kreis",
"Antonio Torralba",
"Sanja Fidler"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Semantic_Segmentation_With_Generative_Models_Semi-Supervised_Learning_and_Strong_Out-of-Domain_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Semantic_Segmentation_With_Generative_Models_Semi-Supervised_Learning_and_Strong_Out-of-Domain_CVPR_2021_paper.pdf | null | 2104.05833 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Daiqing and Yang, Junlin and Kreis, Karsten and Torralba, Antonio and Fidler, Sanja},
title = {Semantic Segmentation With Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts. This is the goal of semi-supervised learning, which exploits more widely available unlabeled data to complement small labeled data sets. In this paper, we propose a no... |
Mei_Depth-Aware_Mirror_Segmentation_CVPR_2021_paper | Depth-Aware Mirror Segmentation | [
"Haiyang Mei",
"Bo Dong",
"Wen Dong",
"Pieter Peers",
"Xin Yang",
"Qiang Zhang",
"Xiaopeng Wei"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Depth-Aware_Mirror_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Depth-Aware_Mirror_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mei_Depth-Aware_Mirror_Segmentation_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Mei_2021_CVPR,
author = {Mei, Haiyang and Dong, Bo and Dong, Wen and Peers, Pieter and Yang, Xin and Zhang, Qiang and Wei, Xiaopeng},
title = {Depth-Aware Mirror Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | We present a novel mirror segmentation method that leverages depth estimates from ToF-based cameras as an additional cue to disambiguate challenging cases where the contrast or relation in RGB colors between the mirror reflection and the surrounding scene is subtle. A key observation is that ToF depth estimates do not ... |
Chen_You_Only_Look_One-Level_Feature_CVPR_2021_paper | You Only Look One-Level Feature | [
"Qiang Chen",
"Yingming Wang",
"Tong Yang",
"Xiangyu Zhang",
"Jian Cheng",
"Jian Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_You_Only_Look_One-Level_Feature_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_You_Only_Look_One-Level_Feature_CVPR_2021_paper.pdf | null | 2103.09460 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian},
title = {You Only Look One-Level Feature},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. From the perspective of optimization, we introduce an alternative way to ad... |
Sepas-Moghaddam_Multi-Perspective_LSTM_for_Joint_Visual_Representation_Learning_CVPR_2021_paper | Multi-Perspective LSTM for Joint Visual Representation Learning | [
"Alireza Sepas-Moghaddam",
"Fernando Pereira",
"Paulo Lobato Correia",
"Ali Etemad"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sepas-Moghaddam_Multi-Perspective_LSTM_for_Joint_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sepas-Moghaddam_Multi-Perspective_LSTM_for_Joint_Visual_Representation_Learning_CVPR_2021_paper.pdf | null | 2105.02802 | title_snapshot | @InProceedings{Sepas-Moghaddam_2021_CVPR,
author = {Sepas-Moghaddam, Alireza and Pereira, Fernando and Correia, Paulo Lobato and Etemad, Ali},
title = {Multi-Perspective LSTM for Joint Visual Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | We present a novel LSTM cell architecture capable of learning both intra- and inter-perspective relationships available in visual sequences captured from multiple perspectives. Our architecture adopts a novel recurrent joint learning strategy that uses additional gates and memories at the cell level. We demonstrate tha... |
Yang_Towards_Improving_the_Consistency_Efficiency_and_Flexibility_of_Differentiable_Neural_CVPR_2021_paper | Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search | [
"Yibo Yang",
"Shan You",
"Hongyang Li",
"Fei Wang",
"Chen Qian",
"Zhouchen Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Towards_Improving_the_Consistency_Efficiency_and_Flexibility_of_Differentiable_Neural_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Towards_Improving_the_Consistency_Efficiency_and_Flexibility_of_Differentiable_Neural_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Towards_Improving_the_CVPR_2021_supplemental.zip | 2101.11342 | cvf | @InProceedings{Yang_2021_CVPR,
author = {Yang, Yibo and You, Shan and Li, Hongyang and Wang, Fei and Qian, Chen and Lin, Zhouchen},
title = {Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for evaluation. There exists a significant gap between the architectures in search and evaluation. As a result, current methods suffer from an inconsistent, inefficient, and inflexible search... |
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