点击上方“CVer”，选择加"星标"置顶
重磅干货，第一时间送达
整理不易，希望点个在看或者转发，支持一下
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，全在这里了！
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
下载
在CVer公众号后台回复：CVPR2020，即可下载上述内容
https://github.com/amusi/CVPR2020-Code
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数据集
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