本文转自https://github.com/amusi/CVPR2020-Code，详细信息请跳转至该网址，有更多惊喜。
前言
之前Amusi整理了1467篇CVPR 2020所有论文PDF下载资源，以及270篇CVPR 2020代码开源论文项目，详见：270篇CVPR 2020代码开源的论文，https://github.com/amusi/daily-paper-computer-vision
CVPR 2020代码开源项目一放出，得到不少CVers的关注，重点是：开源和根据方向分类。目前star数已经来到2000+，期间也有不少国内外的CVPR 2020论文作者提交issue，分享他们的工作。
在此再次更新数据，代码开源的论文突破300+，项目还在持续更新，欢迎补充分享，也推荐大家学习：
https://github.com/amusi/CVPR2020-Code
注：下面内容很硬核，可以在CVer公众号后台回复：CVPR2020，即可下载如下内容
CVPR2020-Code
CNN
图像分类
目标检测
3D目标检测
视频目标检测
目标跟踪
语义分割
实例分割
全景分割
视频目标分割
超像素分割
NAS
GAN
Re-ID
3D点云（分类/分割/配准/跟踪等）
人脸（识别/检测/重建等）
人体姿态估计(2D/3D)
人体解析
场景文本检测
场景文本识别
特征(点)检测和描述
超分辨率
模型压缩/剪枝
视频理解/行为识别
人群计数
深度估计
6D目标姿态估计
手势估计
显著性检测
去噪
去模糊
去雾
特征点检测与描述
视觉问答(VQA)
视频问答(VideoQA)
视觉语言导航
视频压缩
视频插帧
风格迁移
车道线检测
"人-物"交互(HOI)检测
轨迹预测
运动预测
光流估计
图像检索
虚拟试衣
HDR
对抗样本
三维重建
深度补全
语义场景补全
图像/视频描述
线框解析
数据集
其他
CNN
Exploring Self-attention for Image Recognition
论文：https://hszhao.github.io/papers/cvpr20_san.pdf
代码：https://github.com/hszhao/SAN
Improving Convolutional Networks with Self-Calibrated Convolutions
主页：https://mmcheng.net/scconv/
论文：http://mftp.mmcheng.net/Papers/20cvprSCNet.pdf
代码：https://github.com/backseason/SCNet
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets
论文：https://arxiv.org/abs/2003.13549
代码：https://github.com/zeiss-microscopy/BSConv
图像分类
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
论文：https://arxiv.org/abs/2003.04490
代码：https://github.com/AdamKortylewski/CompositionalNets
Spatially Attentive Output Layer for Image Classification
论文：https://arxiv.org/abs/2004.07570
代码（好像被原作者删除了）：https://github.com/ildoonet/spatially-attentive-output-layer
目标检测
Noise-Aware Fully Webly Supervised Object Detection
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.html
代码：https://github.com/shenyunhang/NA-fWebSOD/
Learning a Unified Sample Weighting Network for Object Detection
论文：https://arxiv.org/abs/2006.06568
代码：https://github.com/caiqi/sample-weighting-network
D2Det: Towards High Quality Object Detection and Instance Segmentation
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf
代码：https://github.com/JialeCao001/D2Det
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
论文下载链接：https://arxiv.org/abs/2005.09973
代码和数据集：https://github.com/Anymake/DRN_CVPR2020
Scale-Equalizing Pyramid Convolution for Object Detection
论文：https://arxiv.org/abs/2005.03101
代码：https://github.com/jshilong/SEPC
Revisiting the Sibling Head in Object Detector
论文：https://arxiv.org/abs/2003.07540
代码：https://github.com/Sense-X/TSD
Scale-equalizing Pyramid Convolution for Object Detection
论文：暂无
代码：https://github.com/jshilong/SEPC
Detection in Crowded Scenes: One Proposal, Multiple Predictions
论文：https://arxiv.org/abs/2003.09163
代码：https://github.com/megvii-model/CrowdDetection
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
论文：https://arxiv.org/abs/2004.04725
代码：https://github.com/NVlabs/wetectron
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
论文：https://arxiv.org/abs/1912.02424
代码：https://github.com/sfzhang15/ATSS
BiDet: An Efficient Binarized Object Detector
论文：https://arxiv.org/abs/2003.03961
代码：https://github.com/ZiweiWangTHU/BiDet
Harmonizing Transferability and Discriminability for Adapting Object Detectors
论文：https://arxiv.org/abs/2003.06297
代码：https://github.com/chaoqichen/HTCN
CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection
论文：https://arxiv.org/abs/2003.09119
代码：https://github.com/KiveeDong/CentripetalNet
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
论文：https://arxiv.org/abs/2003.11818
代码：https://github.com/ggjy/HitDet.pytorch
EfficientDet: Scalable and Efficient Object Detection
论文：https://arxiv.org/abs/1911.09070
代码：https://github.com/google/automl/tree/master/efficientdet
3D目标检测
Structure Aware Single-stage 3D Object Detection from Point Cloud
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/He_Structure_Aware_Single-Stage_3D_Object_Detection_From_Point_Cloud_CVPR_2020_paper.html
代码：https://github.com/skyhehe123/SA-SSD
IDA-3D: Instance-Depth-Aware 3D Object Detection from Stereo Vision for Autonomous Driving
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Peng_IDA-3D_Instance-Depth-Aware_3D_Object_Detection_From_Stereo_Vision_for_Autonomous_CVPR_2020_paper.pdf
代码：https://github.com/swords123/IDA-3D
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
论文：https://arxiv.org/abs/2005.08139
代码：https://github.com/cxy1997/3D_adapt_auto_driving
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
论文：https://arxiv.org/abs/2004.05679
代码：https://github.com/NUAAXQ/MLCVNet
3DSSD: Point-based 3D Single Stage Object Detector
CVPR 2020 Oral
论文：https://arxiv.org/abs/2002.10187
代码：https://github.com/tomztyang/3DSSD
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
论文：https://arxiv.org/abs/2004.03572
代码：https://github.com/zju3dv/disprcn
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
论文：https://arxiv.org/abs/2004.03080
代码：https://github.com/mileyan/pseudo-LiDAR_e2e
DSGN: Deep Stereo Geometry Network for 3D Object Detection
论文：https://arxiv.org/abs/2001.03398
代码：https://github.com/chenyilun95/DSGN
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
论文：https://arxiv.org/abs/2004.01389
代码：https://github.com/yinjunbo/3DVID
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
论文：https://arxiv.org/abs/1912.13192
代码：https://github.com/sshaoshuai/PV-RCNN
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
论文：https://arxiv.org/abs/2003.01251
代码：https://github.com/WeijingShi/Point-GNN
视频目标检测
Memory Enhanced Global-Local Aggregation for Video Object Detection
论文：https://arxiv.org/abs/2003.12063
代码：https://github.com/Scalsol/mega.pytorch
目标跟踪
SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
论文：https://arxiv.org/abs/1911.07241
代码：https://github.com/ohhhyeahhh/SiamCAR
D3S – A Discriminative Single Shot Segmentation Tracker
论文：https://arxiv.org/abs/1911.08862
代码：https://github.com/alanlukezic/d3s
ROAM: Recurrently Optimizing Tracking Model
论文：https://arxiv.org/abs/1907.12006
代码：https://github.com/skyoung/ROAM
Siam R-CNN: Visual Tracking by Re-Detection
主页：https://www.vision.rwth-aachen.de/page/siamrcnn
论文：https://arxiv.org/abs/1911.12836
论文2：https://www.vision.rwth-aachen.de/media/papers/192/siamrcnn.pdf
代码：https://github.com/VisualComputingInstitute/SiamR-CNN
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises
论文：https://arxiv.org/abs/2003.09595
代码：https://github.com/MasterBin-IIAU/CSA
High-Performance Long-Term Tracking with Meta-Updater
论文：https://arxiv.org/abs/2004.00305
代码：https://github.com/Daikenan/LTMU
AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization
论文：https://arxiv.org/abs/2003.12949
代码：https://github.com/vision4robotics/AutoTrack
Probabilistic Regression for Visual Tracking
论文：https://arxiv.org/abs/2003.12565
代码：https://github.com/visionml/pytracking
MAST: A Memory-Augmented Self-supervised Tracker
论文：https://arxiv.org/abs/2002.07793
代码：https://github.com/zlai0/MAST
Siamese Box Adaptive Network for Visual Tracking
论文：https://arxiv.org/abs/2003.06761
代码：https://github.com/hqucv/siamban
语义分割
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
论文：暂无
代码：https://github.com/JianqiangWan/Super-BPD
Single-Stage Semantic Segmentation from Image Labels
论文：https://arxiv.org/abs/2005.08104
代码：https://github.com/visinf/1-stage-wseg
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
论文：https://arxiv.org/abs/2003.00867
代码：https://github.com/MyeongJin-Kim/Learning-Texture-Invariant-Representation
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
论文：http://vladlen.info/papers/MSeg.pdf
代码：https://github.com/mseg-dataset/mseg-api
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
论文：https://arxiv.org/abs/2005.02551
代码：https://github.com/hkchengrex/CascadePSP
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Oral
论文：https://arxiv.org/abs/2004.07703
代码：https://github.com/feipan664/IntraDA
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
论文：https://arxiv.org/abs/2004.04581
代码：https://github.com/YudeWang/SEAM
Temporally Distributed Networks for Fast Video Segmentation
论文：https://arxiv.org/abs/2004.01800
代码：https://github.com/feinanshan/TDNet
Context Prior for Scene Segmentation
论文：https://arxiv.org/abs/2004.01547
代码：https://git.io/ContextPrior
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing
论文：https://arxiv.org/abs/2003.13328
代码：https://github.com/Andrew-Qibin/SPNet
Cars Can’t Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
论文：https://arxiv.org/abs/2003.05128
代码：https://github.com/shachoi/HANet
Learning Dynamic Routing for Semantic Segmentation
论文：https://arxiv.org/abs/2003.10401
代码：https://github.com/yanwei-li/DynamicRouting
实例分割
D2Det: Towards High Quality Object Detection and Instance Segmentation
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf
代码：https://github.com/JialeCao001/D2Det
PolarMask: Single Shot Instance Segmentation with Polar Representation
论文：https://arxiv.org/abs/1909.13226
代码：https://github.com/xieenze/PolarMask
解读：https://zhuanlan.zhihu.com/p/84890413
CenterMask : Real-Time Anchor-Free Instance Segmentation
论文：https://arxiv.org/abs/1911.06667
代码：https://github.com/youngwanLEE/CenterMask
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
论文：https://arxiv.org/abs/2001.00309
代码：https://github.com/aim-uofa/AdelaiDet
Deep Snake for Real-Time Instance Segmentation
论文：https://arxiv.org/abs/2001.01629
代码：https://github.com/zju3dv/snake
Mask Encoding for Single Shot Instance Segmentation
论文：https://arxiv.org/abs/2003.11712
代码：https://github.com/aim-uofa/AdelaiDet
全景分割
Pixel Consensus Voting for Panoptic Segmentation
论文：https://arxiv.org/abs/2004.01849
代码：还未公布
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
论文：https://arxiv.org/abs/2003.14031
代码：https://github.com/Mooonside/BANet
视频目标分割
A Transductive Approach for Video Object Segmentation
论文：https://arxiv.org/abs/2004.07193
代码：https://github.com/microsoft/transductive-vos.pytorch
State-Aware Tracker for Real-Time Video Object Segmentation
论文：https://arxiv.org/abs/2003.00482
代码：https://github.com/MegviiDetection/video_analyst
Learning Fast and Robust Target Models for Video Object Segmentation
论文：https://arxiv.org/abs/2003.00908
代码：https://github.com/andr345/frtm-vos
Learning Video Object Segmentation from Unlabeled Videos
论文：https://arxiv.org/abs/2003.05020
代码：https://github.com/carrierlxk/MuG
超像素分割
Superpixel Segmentation with Fully Convolutional Networks
论文：https://arxiv.org/abs/2003.12929
代码：https://github.com/fuy34/superpixel_fcn
NAS
AOWS: Adaptive and optimal network width search with latency constraints
论文：https://arxiv.org/abs/2005.10481
代码：https://github.com/bermanmaxim/AOWS
Densely Connected Search Space for More Flexible Neural Architecture Search
论文：https://arxiv.org/abs/1906.09607
代码：https://github.com/JaminFong/DenseNAS
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
论文：https://arxiv.org/abs/2003.14058
代码：https://github.com/bhpfelix/MTLNAS
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
论文下载链接：https://arxiv.org/abs/2004.05565
代码：https://github.com/facebookresearch/mobile-vision
Neural Architecture Search for Lightweight Non-Local Networks
论文：https://arxiv.org/abs/2004.01961
代码：https://github.com/LiYingwei/AutoNL
Rethinking Performance Estimation in Neural Architecture Search
论文：https://arxiv.org/abs/2005.09917
代码：https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS
解读1：https://www.zhihu.com/question/372070853/answer/1035234510
解读2：https://zhuanlan.zhihu.com/p/111167409
CARS: Continuous Evolution for Efficient Neural Architecture Search
论文：https://arxiv.org/abs/1909.04977
代码（即将开源）：https://github.com/huawei-noah/CARS
GAN
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
论文：https://arxiv.org/abs/1912.01899
代码：https://github.com/SsGood/DBGAN
PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer
论文：https://arxiv.org/abs/1909.06956
代码：https://github.com/wtjiang98/PSGAN
Semantically Mutil-modal Image Synthesis
主页：http://seanseattle.github.io/SMIS
论文：https://arxiv.org/abs/2003.12697
代码：https://github.com/Seanseattle/SMIS
Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping
论文：https://yiranran.github.io/files/CVPR2020_Unpaired%20Portrait%20Drawing%20Generation%20via%20Asymmetric%20Cycle%20Mapping.pdf
代码：https://github.com/yiranran/Unpaired-Portrait-Drawing
Learning to Cartoonize Using White-box Cartoon Representations
论文：https://github.com/SystemErrorWang/White-box-Cartoonization/blob/master/paper/06791.pdf
主页：https://systemerrorwang.github.io/White-box-Cartoonization/
代码：https://github.com/SystemErrorWang/White-box-Cartoonization
解读：https://zhuanlan.zhihu.com/p/117422157
Demo视频：https://www.bilibili.com/video/av56708333
GAN Compression: Efficient Architectures for Interactive Conditional GANs
论文：https://arxiv.org/abs/2003.08936
代码：https://github.com/mit-han-lab/gan-compression
Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions
论文：https://arxiv.org/abs/2003.01826
代码：https://github.com/cc-hpc-itwm/UpConv
Re-ID
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
论文：https://arxiv.org/abs/2005.07862
数据集：暂无
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
论文：https://arxiv.org/abs/2004.04199
代码：https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking
Pose-guided Visible Part Matching for Occluded Person ReID
论文：https://arxiv.org/abs/2004.00230
代码：https://github.com/hh23333/PVPM
Weakly supervised discriminative feature learning with state information for person identification
论文：https://arxiv.org/abs/2002.11939
代码：https://github.com/KovenYu/state-information
3D点云（分类/分割/配准等）
3D点云卷积
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
论文：https://arxiv.org/abs/2003.00492
代码：https://github.com/yanx27/PointASNL
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
论文下载链接：https://arxiv.org/abs/2003.12971
代码：https://github.com/raoyongming/PointGLR
Grid-GCN for Fast and Scalable Point Cloud Learning
论文：https://arxiv.org/abs/1912.02984
代码：https://github.com/Xharlie/Grid-GCN
FPConv: Learning Local Flattening for Point Convolution
论文：https://arxiv.org/abs/2002.10701
代码：https://github.com/lyqun/FPConv
3D点云分类
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
论文：https://arxiv.org/abs/2002.10876
代码（即将开源）：https://github.com/liruihui/PointAugment/
3D点云语义分割
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
论文：https://arxiv.org/abs/1911.11236
代码：https://github.com/QingyongHu/RandLA-Net
解读：https://zhuanlan.zhihu.com/p/105433460
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
论文：https://arxiv.org/abs/2004.0409
代码：https://github.com/alex-xun-xu/WeakSupPointCloudSeg
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
论文：https://arxiv.org/abs/2003.14032
代码：https://github.com/edwardzhou130/PolarSeg
Learning to Segment 3D Point Clouds in 2D Image Space
论文：https://arxiv.org/abs/2003.05593
代码：https://github.com/WPI-VISLab/Learning-to-Segment-3D-Point-Clouds-in-2D-Image-Space
3D点云实例分割
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
论文：https://arxiv.org/abs/2004.01658
代码：https://github.com/Jia-Research-Lab/PointGroup
3D点云配准
D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features
论文：https://arxiv.org/abs/2003.03164
代码：https://github.com/XuyangBai/D3Feat
RPM-Net: Robust Point Matching using Learned Features
论文：https://arxiv.org/abs/2003.13479
代码：https://github.com/yewzijian/RPMNet
3D点云补全
Cascaded Refinement Network for Point Cloud Completion
论文：https://arxiv.org/abs/2004.03327
代码：https://github.com/xiaogangw/cascaded-point-completion
3D点云目标跟踪
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
论文：https://arxiv.org/abs/2005.13888
代码：https://github.com/HaozheQi/P2B
人脸
人脸识别
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
论文：https://arxiv.org/abs/2004.00288
代码：https://github.com/HuangYG123/CurricularFace
Learning Meta Face Recognition in Unseen Domains
论文：https://arxiv.org/abs/2003.07733
代码：https://github.com/cleardusk/MFR
解读：https://mp.weixin.qq.com/s/YZoEnjpnlvb90qSI3xdJqQ
人脸检测
人脸活体检测
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
论文：https://arxiv.org/abs/2003.04092
代码：https://github.com/ZitongYu/CDCN
人脸表情识别
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
论文：https://arxiv.org/abs/2002.10392
代码（即将开源）：https://github.com/kaiwang960112/Self-Cure-Network
人脸转正
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
论文：https://arxiv.org/abs/2003.08124
代码：https://github.com/Hangz-nju-cuhk/Rotate-and-Render
人脸3D重建
AvatarMe: Realistically Renderable 3D Facial Reconstruction “in-the-wild”
论文：https://arxiv.org/abs/2003.13845
数据集：https://github.com/lattas/AvatarMe
FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
论文：https://arxiv.org/abs/2003.13989
代码：https://github.com/zhuhao-nju/facescape
人体姿态估计(2D/3D)
2D人体姿态估计
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
论文：https://arxiv.org/abs/1908.10357
代码：https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation
The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
论文：https://arxiv.org/abs/1911.07524
代码：https://github.com/HuangJunJie2017/UDP-Pose
解读：https://zhuanlan.zhihu.com/p/92525039
Distribution-Aware Coordinate Representation for Human Pose Estimation
主页：https://ilovepose.github.io/coco/
论文：https://arxiv.org/abs/1910.06278
代码：https://github.com/ilovepose/DarkPose
3D人体姿态估计
Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
主页：https://www.zhe-zhang.com/cvpr2020
论文：https://arxiv.org/abs/2003.11163
代码：https://github.com/CHUNYUWANG/imu-human-pose-pytorch
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
论文下载链接：https://arxiv.org/abs/2004.01166
代码：https://github.com/Healthcare-Robotics/bodies-at-rest
数据集：https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KOA4ML
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis
主页：http://val.cds.iisc.ac.in/pgp-human/
论文：https://arxiv.org/abs/2004.04400
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation
论文：https://arxiv.org/abs/2004.00329
代码：https://github.com/fabbrimatteo/LoCO
VIBE: Video Inference for Human Body Pose and Shape Estimation
论文：https://arxiv.org/abs/1912.05656
代码：https://github.com/mkocabas/VIBE
Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation
论文：https://arxiv.org/abs/2002.11251
代码：https://github.com/vnmr/JointVideoPose3D
Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
论文：https://arxiv.org/abs/2003.03972
数据集：暂无
人体解析
Correlating Edge, Pose with Parsing
论文：https://arxiv.org/abs/2005.01431
代码：https://github.com/ziwei-zh/CorrPM
场景文本检测
ContourNet: Taking a Further Step Toward Accurate Arbitrary-Shaped Scene Text Detection
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_ContourNet_Taking_a_Further_Step_Toward_Accurate_Arbitrary-Shaped_Scene_Text_CVPR_2020_paper.pdf
代码：https://github.com/wangyuxin87/ContourNet
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
论文：https://arxiv.org/abs/2003.10608
代码和数据集：https://github.com/Jyouhou/UnrealText/
ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
论文：https://arxiv.org/abs/2002.10200
代码（即将开源）：https://github.com/Yuliang-Liu/bezier_curve_text_spotting
代码（即将开源）：https://github.com/aim-uofa/adet
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
论文：https://arxiv.org/abs/2003.07493
代码：https://github.com/GXYM/DRRG
场景文本识别
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
论文：https://arxiv.org/abs/2005.10977
代码：https://github.com/Pay20Y/SEED
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
论文：https://arxiv.org/abs/2003.10608
代码和数据集：https://github.com/Jyouhou/UnrealText/
ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
论文：https://arxiv.org/abs/2002.10200
代码（即将开源）：https://github.com/aim-uofa/adet
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
论文：https://arxiv.org/abs/2003.06606
代码：https://github.com/Canjie-Luo/Text-Image-Augmentation
特征(点)检测和描述
SuperGlue: Learning Feature Matching with Graph Neural Networks
论文：https://arxiv.org/abs/1911.11763
代码：https://github.com/magicleap/SuperGluePretrainedNetwork
超分辨率
图像超分辨率
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Closed-Loop_Matters_Dual_Regression_Networks_for_Single_Image_Super-Resolution_CVPR_2020_paper.html
代码：https://github.com/guoyongcs/DRN
Learning Texture Transformer Network for Image Super-Resolution
论文：https://arxiv.org/abs/2006.04139
代码：https://github.com/FuzhiYang/TTSR
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
论文：https://arxiv.org/abs/2006.01424
代码：https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention
Structure-Preserving Super Resolution with Gradient Guidance
论文：https://arxiv.org/abs/2003.13081
代码：https://github.com/Maclory/SPSR
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
论文：https://arxiv.org/abs/2004.00448
代码：https://github.com/clovaai/cutblur
视频超分辨率
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution
论文：https://arxiv.org/abs/1812.02898
代码：https://github.com/YapengTian/TDAN-VSR-CVPR-2020
Space-Time-Aware Multi-Resolution Video Enhancement
主页：https://alterzero.github.io/projects/STAR.html
论文：http://arxiv.org/abs/2003.13170
代码：https://github.com/alterzero/STARnet
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
论文：https://arxiv.org/abs/2002.11616
代码：https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
模型压缩/剪枝
DMCP: Differentiable Markov Channel Pruning for Neural Networks
论文：https://arxiv.org/abs/2005.03354
代码：https://github.com/zx55/dmcp
Forward and Backward Information Retention for Accurate Binary Neural Networks
论文：https://arxiv.org/abs/1909.10788
代码：https://github.com/htqin/IR-Net
Towards Efficient Model Compression via Learned Global Ranking
论文：https://arxiv.org/abs/1904.12368
代码：https://github.com/cmu-enyac/LeGR
HRank: Filter Pruning using High-Rank Feature Map
论文：http://arxiv.org/abs/2002.10179
代码：https://github.com/lmbxmu/HRank
GAN Compression: Efficient Architectures for Interactive Conditional GANs
论文：https://arxiv.org/abs/2003.08936
代码：https://github.com/mit-han-lab/gan-compression
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
论文：https://arxiv.org/abs/2003.08935
代码：https://github.com/ofsoundof/group_sparsity
视频理解/行为识别
Oops! Predicting Unintentional Action in Video
主页：https://oops.cs.columbia.edu/
论文：https://arxiv.org/abs/1911.11206
代码：https://github.com/cvlab-columbia/oops
数据集：https://oops.cs.columbia.edu/data
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
论文：https://arxiv.org/abs/1911.12409
代码：https://github.com/shlizee/Predict-Cluster
Intra- and Inter-Action Understanding via Temporal Action Parsing
论文：https://arxiv.org/abs/2005.10229
主页和数据集：https://sdolivia.github.io/TAPOS/
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
论文：https://arxiv.org/abs/2005.05501
代码：https://github.com/3huo/3DV-Action
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
主页：https://sdolivia.github.io/FineGym/
论文：https://arxiv.org/abs/2004.06704
TEA: Temporal Excitation and Aggregation for Action Recognition
论文：https://arxiv.org/abs/2004.01398
代码：https://github.com/Phoenix1327/tea-action-recognition
X3D: Expanding Architectures for Efficient Video Recognition
论文：https://arxiv.org/abs/2004.04730
代码：https://github.com/facebookresearch/SlowFast
Temporal Pyramid Network for Action Recognition
主页：https://decisionforce.github.io/TPN
论文：https://arxiv.org/abs/2004.03548
代码：https://github.com/decisionforce/TPN
基于骨架的动作识别
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
论文：https://arxiv.org/abs/2003.14111
代码：https://github.com/kenziyuliu/ms-g3d
人群计数
深度估计
BiFuse: Monocular 360◦ Depth Estimation via Bi-Projection Fusion
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_BiFuse_Monocular_360_Depth_Estimation_via_Bi-Projection_Fusion_CVPR_2020_paper.pdf
代码：https://github.com/Yeh-yu-hsuan/BiFuse
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
论文：https://arxiv.org/abs/2005.09623
代码：https://github.com/dvl-tum/defocus-net
Bi3D: Stereo Depth Estimation via Binary Classifications
论文：https://arxiv.org/abs/2005.07274
代码：https://github.com/NVlabs/Bi3D
AANet: Adaptive Aggregation Network for Efficient Stereo Matching
论文：https://arxiv.org/abs/2004.09548
代码：https://github.com/haofeixu/aanet
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
论文：https://github.com/B1ueber2y/TrianFlow
代码：https://github.com/B1ueber2y/TrianFlow
单目深度估计
On the uncertainty of self-supervised monocular depth estimation
论文：https://arxiv.org/abs/2005.06209
代码：https://github.com/mattpoggi/mono-uncertainty
3D Packing for Self-Supervised Monocular Depth Estimation
论文：https://arxiv.org/abs/1905.02693
代码：https://github.com/TRI-ML/packnet-sfm
Demo视频：https://www.bilibili.com/video/av70562892/
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation
论文：https://arxiv.org/abs/2002.12114
代码：https://github.com/yzhao520/ARC
6D目标姿态估计
MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion
论文：https://arxiv.org/abs/2004.04336
代码：https://github.com/wkentaro/morefusion
EPOS: Estimating 6D Pose of Objects with Symmetries
主页：http://cmp.felk.cvut.cz/epos
论文：https://arxiv.org/abs/2004.00605
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
论文：https://arxiv.org/abs/2003.11089
代码：https://github.com/DC1991/G2L_Net
手势估计
HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation
论文：https://arxiv.org/abs/2004.00060
主页：http://vision.sice.indiana.edu/projects/hopenet
Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data
论文：https://arxiv.org/abs/2003.09572
代码：https://github.com/CalciferZh/minimal-hand
显著性检测
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
论文：https://arxiv.org/abs/2004.08515
代码：https://github.com/kerenfu/JLDCF/
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
主页：http://dpfan.net/d3netbenchmark/
论文：https://arxiv.org/abs/2004.05763
代码：https://github.com/JingZhang617/UCNet
去噪
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising
论文：https://arxiv.org/abs/2003.12751
代码：https://github.com/Vandermode/NoiseModel
CycleISP: Real Image Restoration via Improved Data Synthesis
论文：https://arxiv.org/abs/2003.07761
代码：https://github.com/swz30/CycleISP
去雨
Multi-Scale Progressive Fusion Network for Single Image Deraining
论文：https://arxiv.org/abs/2003.10985
代码：https://github.com/kuihua/MSPFN
去模糊
视频去模糊
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior
主页：https://csbhr.github.io/projects/cdvd-tsp/index.html
论文：https://arxiv.org/abs/2004.02501
代码：https://github.com/csbhr/CDVD-TSP
去雾
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
论文：https://arxiv.org/abs/2004.13388
代码：https://github.com/BookerDeWitt/MSBDN-DFF
特征点检测与描述
ASLFeat: Learning Local Features of Accurate Shape and Localization
论文：https://arxiv.org/abs/2003.10071
代码：https://github.com/lzx551402/aslfeat
视觉问答(VQA)
VC R-CNN：Visual Commonsense R-CNN
论文：https://arxiv.org/abs/2002.12204
代码：https://github.com/Wangt-CN/VC-R-CNN
视频问答(VideoQA)
Hierarchical Conditional Relation Networks for Video Question Answering
论文：https://arxiv.org/abs/2002.10698
代码：https://github.com/thaolmk54/hcrn-videoqa
视觉语言导航
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training
论文：https://arxiv.org/abs/2002.10638
代码（即将开源）：https://github.com/weituo12321/PREVALENT
视频压缩
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
论文：https://arxiv.org/abs/2003.01966
代码：https://github.com/RenYang-home/HLVC
视频插帧
FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.html
代码：https://github.com/CM-BF/FeatureFlow
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
论文：https://arxiv.org/abs/2002.11616
代码：https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
Space-Time-Aware Multi-Resolution Video Enhancement
主页：https://alterzero.github.io/projects/STAR.html
论文：http://arxiv.org/abs/2003.13170
代码：https://github.com/alterzero/STARnet
Scene-Adaptive Video Frame Interpolation via Meta-Learning
论文：https://arxiv.org/abs/2004.00779
代码：https://github.com/myungsub/meta-interpolation
Softmax Splatting for Video Frame Interpolation
主页：http://sniklaus.com/papers/softsplat
论文：https://arxiv.org/abs/2003.05534
代码：https://github.com/sniklaus/softmax-splatting
风格迁移
Diversified Arbitrary Style Transfer via Deep Feature Perturbation
论文：https://arxiv.org/abs/1909.08223
代码：https://github.com/EndyWon/Deep-Feature-Perturbation
Collaborative Distillation for Ultra-Resolution Universal Style Transfer
论文：https://arxiv.org/abs/2003.08436
代码：https://github.com/mingsun-tse/collaborative-distillation
车道线检测
Inter-Region Affinity Distillation for Road Marking Segmentation
论文：https://arxiv.org/abs/2004.05304
代码：https://github.com/cardwing/Codes-for-IntRA-KD
"人-物"交互(HOT)检测
PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection
论文：https://arxiv.org/abs/1912.12898
代码：https://github.com/YueLiao/PPDM
Detailed 2D-3D Joint Representation for Human-Object Interaction
论文：https://arxiv.org/abs/2004.08154
代码：https://github.com/DirtyHarryLYL/DJ-RN
Cascaded Human-Object Interaction Recognition
论文：https://arxiv.org/abs/2003.04262
代码：https://github.com/tfzhou/C-HOI
VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
论文：https://arxiv.org/abs/2003.05541
代码：https://github.com/ASMIftekhar/VSGNet
轨迹预测
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
论文：https://arxiv.org/abs/1912.06445
代码：https://github.com/JunweiLiang/Multiverse
数据集：https://next.cs.cmu.edu/multiverse/
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
论文：https://arxiv.org/abs/2002.11927
代码：https://github.com/abduallahmohamed/Social-STGCNN
运动预测
Collaborative Motion Prediction via Neural Motion Message Passing
论文：https://arxiv.org/abs/2003.06594
代码：https://github.com/PhyllisH/NMMP
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps
论文：https://arxiv.org/abs/2003.06754
代码：https://github.com/pxiangwu/MotionNet
光流估计
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
论文：https://arxiv.org/abs/2003.13045
代码：https://github.com/lliuz/ARFlow
图像检索
Evade Deep Image Retrieval by Stashing Private Images in the Hash Space
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Xiao_Evade_Deep_Image_Retrieval_by_Stashing_Private_Images_in_the_CVPR_2020_paper.html
代码：https://github.com/sugarruy/hashstash
虚拟试衣
Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content
论文：https://arxiv.org/abs/2003.05863
代码：https://github.com/switchablenorms/DeepFashion_Try_On
HDR
Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline
主页：https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR
论文下载链接：https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR_/00942.pdf
代码：https://github.com/alex04072000/SingleHDR
对抗样本
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
论文：https://arxiv.org/abs/1911.02466
代码：https://github.com/ZhengyuZhao/PerC-Adversarial
三维重建
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
CVPR 2020 Best Paper
主页：https://elliottwu.com/projects/unsup3d/
论文：https://arxiv.org/abs/1911.11130
代码：https://github.com/elliottwu/unsup3d
Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
主页：https://shunsukesaito.github.io/PIFuHD/
论文：https://arxiv.org/abs/2004.00452
代码：https://github.com/facebookresearch/pifuhd
深度补全
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
论文：https://arxiv.org/abs/2006.03349
代码：https://github.com/abdo-eldesokey/pncnn
语义场景补全
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
论文：https://arxiv.org/abs/2003.14052
代码：https://github.com/charlesCXK/3D-SketchAware-SSC
图像/视频描述
Syntax-Aware Action Targeting for Video Captioning
论文：http://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Syntax-Aware_Action_Targeting_for_Video_Captioning_CVPR_2020_paper.pdf
代码：https://github.com/SydCaption/SAAT
线框解析
Holistically-Attracted Wireframe Parser
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Holistically-Attracted_Wireframe_Parsing_CVPR_2020_paper.html
代码：https://github.com/cherubicXN/hawp
数据集
Oops! Predicting Unintentional Action in Video
主页：https://oops.cs.columbia.edu/
论文：https://arxiv.org/abs/1911.11206
代码：https://github.com/cvlab-columbia/oops
数据集：https://oops.cs.columbia.edu/data
The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
论文：https://arxiv.org/abs/1912.06445
代码：https://github.com/JunweiLiang/Multiverse
数据集：https://next.cs.cmu.edu/multiverse/
Open Compound Domain Adaptation
主页：https://liuziwei7.github.io/projects/CompoundDomain.html
数据集：https://drive.google.com/drive/folders/1_uNTF8RdvhS_sqVTnYx17hEOQpefmE2r?usp=sharing
论文：https://arxiv.org/abs/1909.03403
代码：https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA
Intra- and Inter-Action Understanding via Temporal Action Parsing
论文：https://arxiv.org/abs/2005.10229
主页和数据集：https://sdolivia.github.io/TAPOS/
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
论文下载链接：https://arxiv.org/abs/2005.09973
代码和数据集：https://github.com/Anymake/DRN_CVPR2020
COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
论文：https://arxiv.org/abs/2005.07862
数据集：暂无
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
论文：https://arxiv.org/abs/2002.12687
数据集：https://github.com/qq456cvb/KeypointNet
MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
论文：http://vladlen.info/papers/MSeg.pdf
代码：https://github.com/mseg-dataset/mseg-api
数据集：https://github.com/mseg-dataset/mseg-semantic
AvatarMe: Realistically Renderable 3D Facial Reconstruction “in-the-wild”
论文：https://arxiv.org/abs/2003.13845
数据集：https://github.com/lattas/AvatarMe
Learning to Autofocus
论文：https://arxiv.org/abs/2004.12260
数据集：暂无
FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
论文：https://arxiv.org/abs/2003.13989
代码：https://github.com/zhuhao-nju/facescape
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
论文下载链接：https://arxiv.org/abs/2004.01166
代码：https://github.com/Healthcare-Robotics/bodies-at-rest
数据集：https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KOA4ML
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
主页：https://sdolivia.github.io/FineGym/
论文：https://arxiv.org/abs/2004.06704
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
主页：https://anyirao.com/projects/SceneSeg.html
论文下载链接：https://arxiv.org/abs/2004.02678
代码：https://github.com/AnyiRao/SceneSeg
Deep Homography Estimation for Dynamic Scenes
论文：https://arxiv.org/abs/2004.02132
数据集：https://github.com/lcmhoang/hmg-dynamics
Assessing Image Quality Issues for Real-World Problems
主页：https://vizwiz.org/tasks-and-datasets/image-quality-issues/
论文：https://arxiv.org/abs/2003.12511
UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
论文：https://arxiv.org/abs/2003.10608
代码和数据集：https://github.com/Jyouhou/UnrealText/
PANDA: A Gigapixel-level Human-centric Video Dataset
论文：https://arxiv.org/abs/2003.04852
数据集：http://www.panda-dataset.com/
IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning
论文：https://arxiv.org/abs/2003.02920
数据集：https://github.com/intra3d2019/IntrA
Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS
论文：https://arxiv.org/abs/2003.03972
数据集：暂无
其他
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
论文：http://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html
代码：https://github.com/fkluger/consac
Learning to Learn Single Domain Generalization
论文：https://arxiv.org/abs/2003.13216
代码：https://github.com/joffery/M-ADA
Open Compound Domain Adaptation
主页：https://liuziwei7.github.io/projects/CompoundDomain.html
数据集：https://drive.google.com/drive/folders/1_uNTF8RdvhS_sqVTnYx17hEOQpefmE2r?usp=sharing
论文：https://arxiv.org/abs/1909.03403
代码：https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
论文：http://www.cvlibs.net/publications/Niemeyer2020CVPR.pdf
代码：https://github.com/autonomousvision/differentiable_volumetric_rendering
QEBA: Query-Efficient Boundary-Based Blackbox Attack
论文：https://arxiv.org/abs/2005.14137
代码：https://github.com/AI-secure/QEBA
Equalization Loss for Long-Tailed Object Recognition
论文：https://arxiv.org/abs/2003.05176
代码：https://github.com/tztztztztz/eql.detectron2
Instance-aware Image Colorization
主页：https://ericsujw.github.io/InstColorization/
论文：https://arxiv.org/abs/2005.10825
代码：https://github.com/ericsujw/InstColorization
Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
论文：https://arxiv.org/abs/2005.09704
代码：https://github.com/Atlas200dk/sample-imageinpainting-HiFill
Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching
论文：https://arxiv.org/abs/2005.03860
代码：https://github.com/shiyujiao/cross_view_localization_DSM
Epipolar Transformers
论文：https://arxiv.org/abs/2005.04551
代码：https://github.com/yihui-he/epipolar-transformers
Bringing Old Photos Back to Life
主页：http://raywzy.com/Old_Photo/
论文：https://arxiv.org/abs/2004.09484
MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
论文：https://arxiv.org/abs/2003.10955
代码：https://github.com/microsoft/MaskFlownet
Self-Supervised Viewpoint Learning from Image Collections
论文：https://arxiv.org/abs/2004.01793
论文2：https://research.nvidia.com/sites/default/files/pubs/2020-03_Self-Supervised-Viewpoint-Learning/SSV-CVPR2020.pdf
代码：https://github.com/NVlabs/SSV
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Oral
论文：https://arxiv.org/abs/2003.12237
代码：https://github.com/cuishuhao/BNM
Towards Learning Structure via Consensus for Face Segmentation and Parsing
论文：https://arxiv.org/abs/1911.00957
代码：https://github.com/isi-vista/structure_via_consensus
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
Oral
论文：https://arxiv.org/abs/2003.13654
代码：https://github.com/liuyang12/PnP-SCI
Lightweight Photometric Stereo for Facial Details Recovery
论文：https://arxiv.org/abs/2003.12307
代码：https://github.com/Juyong/FacePSNet
Footprints and Free Space from a Single Color Image
论文：https://arxiv.org/abs/2004.06376
代码：https://github.com/nianticlabs/footprints
Self-Supervised Monocular Scene Flow Estimation
论文：https://arxiv.org/abs/2004.04143
代码：https://github.com/visinf/self-mono-sf
Quasi-Newton Solver for Robust Non-Rigid Registration
论文：https://arxiv.org/abs/2004.04322
代码：https://github.com/Juyong/Fast_RNRR
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
主页：https://anyirao.com/projects/SceneSeg.html
论文下载链接：https://arxiv.org/abs/2004.02678
代码：https://github.com/AnyiRao/SceneSeg
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration
论文：https://arxiv.org/abs/2004.02097
代码：https://github.com/jw4hv/deepflash
Self-Supervised Scene De-occlusion
主页：https://xiaohangzhan.github.io/projects/deocclusion/
论文：https://arxiv.org/abs/2004.02788
代码：https://github.com/XiaohangZhan/deocclusion
Polarized Reflection Removal with Perfect Alignment in the Wild
主页：https://leichenyang.weebly.com/project-polarized.html
代码：https://github.com/ChenyangLEI/CVPR2020-Polarized-Reflection-Removal-with-Perfect-Alignment
Background Matting: The World is Your Green Screen
论文：https://arxiv.org/abs/2004.00626
代码：http://github.com/senguptaumd/Background-Matting
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
论文：https://arxiv.org/abs/2003.11241
代码：https://github.com/ZhangLi-CS/GCP_Optimization
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
论文：暂无
代码：https://github.com/JDAI-CV/LIO
Video Object Grounding using Semantic Roles in Language Description
论文：https://arxiv.org/abs/2003.10606
代码：https://github.com/TheShadow29/vognet-pytorch
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives
论文：https://arxiv.org/abs/2003.10739
代码：https://github.com/d-li14/DHM
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
论文：http://www.cs.umd.edu/~yuejiang/papers/SDFDiff.pdf
代码：https://github.com/YueJiang-nj/CVPR2020-SDFDiff
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
论文：https://arxiv.org/abs/2003.07064
代码：https://github.com/oskyhn/CNNs-Without-Borders
GhostNet: More Features from Cheap Operations
论文：https://arxiv.org/abs/1911.11907
代码：https://github.com/iamhankai/ghostnet
AdderNet: Do We Really Need Multiplications in Deep Learning?
论文：https://arxiv.org/abs/1912.13200
代码：https://github.com/huawei-noah/AdderNet
Deep Image Harmonization via Domain Verification
论文：https://arxiv.org/abs/1911.13239
代码：https://github.com/bcmi/Image_Harmonization_Datasets
Blurry Video Frame Interpolation
论文：https://arxiv.org/abs/2002.12259
代码：https://github.com/laomao0/BIN
Extremely Dense Point Correspondences using a Learned Feature Descriptor
论文：https://arxiv.org/abs/2003.00619
代码：https://github.com/lppllppl920/DenseDescriptorLearning-Pytorch
Filter Grafting for Deep Neural Networks
论文：https://arxiv.org/abs/2001.05868
代码：https://github.com/fxmeng/filter-grafting
论文解读：https://www.zhihu.com/question/372070853/answer/1041569335
Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation
论文：https://arxiv.org/abs/2003.02824
代码：https://github.com/cmhungsteve/SSTDA
Detecting Attended Visual Targets in Video
论文：https://arxiv.org/abs/2003.02501
代码：https://github.com/ejcgt/attention-target-detection
Deep Image Spatial Transformation for Person Image Generation
论文：https://arxiv.org/abs/2003.00696
代码：https://github.com/RenYurui/Global-Flow-Local-Attention
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
论文：https://arxiv.org/abs/2003.01455
代码：https://github.com/bbrattoli/ZeroShotVideoClassification
https://github.com/charlesCXK/3D-SketchAware-SSC
https://github.com/Anonymous20192020/Anonymous_CVPR5767
https://github.com/avirambh/ScopeFlow
https://github.com/csbhr/CDVD-TSP
https://github.com/ymcidence/TBH
https://github.com/yaoyao-liu/mnemonics
https://github.com/meder411/Tangent-Images
https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch
https://github.com/sjmoran/deep_local_parametric_filters
https://github.com/charlesCXK/3D-SketchAware-SSC
https://github.com/bermanmaxim/AOWS
https://github.com/dc3ea9f/look-into-object