CVPR2021最全整理，CVPR2021下载链接，CVPR2021全部论文代码

CVPR2021年的官网：

总结持续更新Github上面：https://github.com/Sophia-11/Awesome-CVPR-Paper

Image-to-image Translation via Hierarchical Style Disentanglement Xinyang Li, Shengchuan Zhang, Jie Hu, Liujuan Cao, Xiaopeng Hong, Xudong Mao, Feiyue Huang, Yongjian Wu, Rongrong Ji https://arxiv.org/abs/2103.01456 https://github.com/imlixinyang/HiSD

FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation https://arxiv.org/pdf/2012.08512.pdf https://tarun005.github.io/FLAVR/Code https://tarun005.github.io/FLAVR/

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford, Tobias Fischer https://arxiv.org/abs/2103.01486

Depth from Camera Motion and Object Detection Brent A. Griffin, Jason J. Corso https://arxiv.org/abs/2103.01468

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers https://arxiv.org/pdf/2011.09094.pdf

Multi-Stage Progressive Image Restoration https://arxiv.org/abs/2102.02808 https://github.com/swz30/MPRNet

Weakly Supervised Learning of Rigid 3D Scene Flow https://arxiv.org/pdf/2102.08945.pdf https://arxiv.org/pdf/2102.08945.pdf https://3dsceneflow.github.io/

Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah https://arxiv.org/abs/2103.01315

Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels https://arxiv.org/abs/2101.05022 https://github.com/naver-ai/relabel_imagenet

Rethinking Channel Dimensions for Efficient Model Design https://arxiv.org/abs/2007.00992 https://github.com/clovaai/rexnet

Coarse-Fine Networks for Temporal Activity Detection in Videos Kumara Kahatapitiya, Michael S. Ryoo https://arxiv.org/abs/2103.01302

A Deep Emulator for Secondary Motion of 3D Characters Mianlun Zheng, Yi Zhou, Duygu Ceylan, Jernej Barbic https://arxiv.org/abs/2103.01261

Fair Attribute Classification through Latent Space De-biasing https://arxiv.org/abs/2012.01469 https://github.com/princetonvisualai/gan-debiasing https://princetonvisualai.github.io/gan-debiasing/

Auto-Exposure Fusion for Single-Image Shadow Removal Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang https://arxiv.org/abs/2103.01255

Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling https://arxiv.org/pdf/2102.06183.pdf https://github.com/jayleicn/ClipBERT

MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing Zhengjue Wang, Hao Zhang, Ziheng Cheng, Bo Chen, Xin Yuan https://arxiv.org/abs/2103.01786

AttentiveNAS: Improving Neural Architecture Search via Attentive https://arxiv.org/pdf/2011.09011.pdf

Diffusion Probabilistic Models for 3D Point Cloud Generation Shitong Luo, Wei Hu https://arxiv.org/abs/2103.01458

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge Francisco Rivera Valverde, Juana Valeria Hurtado, Abhinav Valada https://arxiv.org/abs/2103.01353 http://rl.uni-freiburg.de/research/multimodal-distill

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation https://arxiv.org/abs/2008.00951 https://github.com/eladrich/pixel2style2pixel https://eladrich.github.io/pixel2style2pixel/

Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph Xin Ye, Yezhou Yang https://arxiv.org/abs/2103.01350

RepVGG: Making VGG-style ConvNets Great Again https://arxiv.org/abs/2101.03697 https://github.com/megvii-model/RepVGG

Transformer Interpretability Beyond Attention Visualization https://arxiv.org/pdf/2012.09838.pdf https://github.com/hila-chefer/Transformer-Explainability

PREDATOR: Registration of 3D Point Clouds with Low Overlap https://arxiv.org/pdf/2011.13005.pdf https://github.com/ShengyuH/OverlapPredator https://overlappredator.github.io/
往年2020年论文回归
目标检测

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection 论文地址：https://arxiv.org/abs/1912.02424
代码：https://github.com/sfzhang15/ATSS

Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector 论文地址：https://arxiv.org/abs/1908.01998

图像分割

Semi-Supervised Semantic Image Segmentation with Self-correcting Networks 论文地址：https://arxiv.org/abs/1811.07073

Deep Snake for Real-Time Instance Segmentation 论文地址：https://arxiv.org/abs/2001.01629

CenterMask : Real-Time Anchor-Free Instance Segmentation 论文地址：https://arxiv.org/abs/1911.06667 代码：https://github.com/youngwanLEE/CenterMask

SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks 论文地址：https://arxiv.org/abs/2003.00678

PolarMask: Single Shot Instance Segmentation with Polar Representation 论文地址：https://arxiv.org/abs/1909.13226 代码：https://github.com/xieenze/PolarMask

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation 论文地址：https://arxiv.org/abs/1911.12676

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation 论文地址：https://arxiv.org/abs/2001.00309

人脸识别

Towards Universal Representation Learning for Deep Face Recognition 论文地址：https://arxiv.org/abs/2002.11841

Suppressing Uncertainties for Large-Scale Facial Expression Recognition
论文地址：https://arxiv.org/abs/2002.10392 代码：https://github.com/kaiwang960112/Self-Cure-Network

3.Face X-ray for More General Face Forgery Detection 论文地址：https://arxiv.org/pdf/1912.13458.pdf

目标跟踪

1.ROAM: Recurrently Optimizing Tracking Model 论文地址：https://arxiv.org/abs/1907.12006

三维点云&重建

PF-Net: Point Fractal Network for 3D Point Cloud Completion 论文地址：https://arxiv.org/abs/2003.00410

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification 论文地址：https://arxiv.org/abs/2002.10876 代码：https://github.com/liruihui/PointAugment/

3.Learning multiview 3D point cloud registration 论文地址：https://arxiv.org/abs/2001.05119

C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds 论文地址：https://arxiv.org/abs/1912.07009

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds 论文地址：https://arxiv.org/abs/1911.11236

Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image 论文地址：https://arxiv.org/abs/2002.12212

Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion 论文地址：https://arxiv.org/abs/2003.01456

In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction from 2D Landmarks 论文地址：https://arxiv.org/pdf/1911.11924.pdf

姿态估计

VIBE: Video Inference for Human Body Pose and Shape Estimation 论文地址：https://arxiv.org/abs/1912.05656
代码：https://github.com/mkocabas/VIBE

Distribution-Aware Coordinate Representation for Human Pose Estimation 论文地址：https://arxiv.org/abs/1910.06278
代码：https://github.com/ilovepose/DarkPose

4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras 论文地址：https://arxiv.org/abs/2002.12625

Optimal least-squares solution to the hand-eye calibration problem 论文地址：https://arxiv.org/abs/2002.10838

D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry 论文地址：https://arxiv.org/abs/2003.01060

Multi-Modal Domain Adaptation for Fine-Grained Action Recognition 论文地址：https://arxiv.org/abs/2001.09691

Distribution Aware Coordinate Representation for Human Pose Estimation 论文地址：https://arxiv.org/abs/1910.06278

The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation 论文地址：https://arxiv.org/abs/1911.07524

9.PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation 论文地址：https://arxiv.org/abs/1911.04231

GAN

Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models 论文地址：https://arxiv.org/abs/1911.12287 代码：https://github.com/giannisdaras/ylg

MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis 论文地址：https://arxiv.org/abs/1903.06048

Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory 论文地址：https://arxiv.org/abs/1911.04636

小样本&零样本

Improved Few-Shot Visual Classification 论文地址：https://arxiv.org/pdf/1912.03432.pdf

2.Meta-Transfer Learning for Zero-Shot Super-Resolution 论文地址：https://arxiv.org/abs/2002.12213

弱监督&无监督

Rethinking the Route Towards Weakly Supervised Object Localization 论文地址：https://arxiv.org/abs/2002.11359
NestedVAE: Isolating Common Factors via Weak Supervision 论文地址：https://arxiv.org/abs/2002.11576

3.Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation 论文地址：https://arxiv.org/abs/1911.07450

4.Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction 论文地址：https://arxiv.org/abs/2003.01460

神经网络

Visual Commonsense R-CNN 论文地址：https://arxiv.org/abs/2002.12204

GhostNet: More Features from Cheap Operations 论文地址：https://arxiv.org/abs/1911.11907 代码：https://github.com/iamhankai/ghostnet

Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral 论文地址：https://arxiv.org/abs/2003.01826

模型加速

GPU-Accelerated Mobile Multi-view Style Transfer 论文地址：https://arxiv.org/abs/2003.00706

视觉常识

What it Thinks is Important is Important: Robustness Transfers through Input Gradients 论文地址：https://arxiv.org/abs/1912.05699

2.Attentive Context Normalization for Robust Permutation-Equivariant Learning 论文地址：https://arxiv.org/abs/1907.02545

Bundle Adjustment on a Graph Processor 论文地址：https://arxiv.org/abs/2003.03134 https://github.com/joeaortiz/gbp

Transferring Dense Pose to Proximal Animal Classes 论文地址：https://arxiv.org/abs/2003.00080

Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs 论文地址：https://arxiv.org/abs/2003.00287

Learning in the Frequency Domain 论文地址：https://arxiv.org/abs/2002.12416

7.Filter Grafting for Deep Neural Networks 论文地址：https://arxiv.org/pdf/2001.05868.pdf

8.ClusterFit: Improving Generalization of Visual Representations 论文地址：https://arxiv.org/abs/1912.03330

9.Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction 论文地址：https://arxiv.org/abs/2002.11927

10.Auto-Encoding Twin-Bottleneck Hashing 论文地址：https://arxiv.org/abs/2002.11930

11.Learning Representations by Predicting Bags of Visual Words 论文地址：https://arxiv.org/abs/2002.12247

12.Holistically-Attracted Wireframe Parsing 论文地址：https://arxiv.org/abs/2003.01663

13.A General and Adaptive Robust Loss Function 论文地址：https://arxiv.org/abs/1701.03077

14.A Characteristic Function Approach to Deep Implicit Generative Modeling 论文地址：https://arxiv.org/abs/1909.07425

15.AdderNet: Do We Really Need Multiplications in Deep Learning? 论文地址：https://arxiv.org/pdf/1912.13200

16.12-in-1: Multi-Task Vision and Language Representation Learning 论文地址：https://arxiv.org/abs/1912.02315

17.Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks 论文地址：https://arxiv.org/abs/1912.09393

18.CARS: Contunuous Evolution for Efficient Neural Architecture Search 论文地址：https://arxiv.org/pdf/1909.04977.pdf 代码：https://github.com/huawei-noah/CARS

19.Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training 论文地址：https://arxiv.org/abs/2002.10638 代码：https://github.com/weituo12321/PREVALENT

1.GhostNet: More Features from Cheap Operations（超越Mobilenet v3的架构）论文链接：https://arxiv.org/pdf/1911.11907arxiv.org 模型（在ARM CPU上的表现惊人）：https://github.com/iamhankai/ghostnetgithub.com

We beat other SOTA lightweight CNNs such as MobileNetV3 and FBNet.

AdderNet: Do We Really Need Multiplications in Deep Learning? （加法神经网络）在大规模神经网络和数据集上取得了非常好的表现论文链接：https://arxiv.org/pdf/1912.13200arxiv.org

Frequency Domain Compact 3D Convolutional Neural Networks （3dCNN压缩）论文链接：https://arxiv.org/pdf/1909.04977arxiv.org 开源代码：https://github.com/huawei-noah/CARSgithub.com

A Semi-Supervised Assessor of Neural Architectures （神经网络精度预测器 NAS）

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection （NAS 检测） backbone-neck-head一起搜索，三位一体

CARS: Contunuous Evolution for Efficient Neural Architecture Search (连续进化的NAS) 高效，具备可微和进化的多重优势，且能输出帕累托前研

On Positive-Unlabeled Classification in GAN （PU+GAN）

Learning multiview 3D point cloud registration（3D点云）论文链接：arxiv.org/abs/2001.05119

Multi-Modal Domain Adaptation for Fine-Grained Action Recognition（细粒度动作识别）论文链接：arxiv.org/abs/2001.09691

Action Modifiers:Learning from Adverbs in Instructional Video 论文链接：arxiv.org/abs/1912.06617

PolarMask: Single Shot Instance Segmentation with Polar Representation（实例分割建模）论文链接：arxiv.org/abs/1909.13226 论文解读：https://zhuanlan.zhihu.com/p/84890413 开源代码：https://github.com/xieenze/PolarMask

Rethinking Performance Estimation in Neural Architecture Search（NAS）由于block wise neural architecture search中真正消耗时间的是performance estimation部分，本文针对 block wise的NAS找到了最优参数，速度更快，且相关度更高。

Distribution Aware Coordinate Representation for Human Pose Estimation（人体姿态估计）论文链接：arxiv.org/abs/1910.06278 Github：https://github.com/ilovepose/DarkPose 作者团队主页：https://ilovepose.github.io/coco/

OCR

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network 论文地址：https://arxiv.org/abs/2002.10200 代码：https://github.com/Yuliang-Liu/bezier_curve_text_spotting,https://github.com/aim-uofa/adet

图像分类

Self-training with Noisy Student improves ImageNet classification 论文地址：https://arxiv.org/abs/1911.04252

Image Matching across Wide Baselines: From Paper to Practice 论文地址：https://arxiv.org/abs/2003.01587

Towards Robust Image Classification Using Sequential Attention Models 论文地址：https://arxiv.org/abs/1912.02184

视频分析

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications 论文地址：https://arxiv.org/abs/2003.01455
代码：https://github.com/bbrattoli/ZeroShotVideoClassification

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs 论文地址：https://arxiv.org/abs/2003.00387

Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning 论文地址：https://arxiv.org/abs/2003.00392

Object Relational Graph with Teacher-Recommended Learning for Video Captioning 论文地址：https://arxiv.org/abs/2002.11566

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution 论文地址：https://arxiv.org/abs/2002.11616

Blurry Video Frame Interpolation 论文地址：https://arxiv.org/abs/2002.12259

Hierarchical Conditional Relation Networks for Video Question Answering 论文地址：https://arxiv.org/abs/2002.10698

Action Modifiers:Learning from Adverbs in Instructional Video 论文地址：https://arxiv.org/abs/1912.06617

图像处理

Learning to Shade Hand-drawn Sketches 论文地址：https://arxiv.org/abs/2002.11812

2.Single Image Reflection Removal through Cascaded Refinement 论文地址：https://arxiv.org/abs/1911.06634

3.Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data 论文地址：https://arxiv.org/abs/2002.11297

Deep Image Harmonization via Domain Verification 论文地址：https://arxiv.org/abs/1911.13239 代码：https://github.com/bcmi/Image_Harmonization_Datasets

RoutedFusion: Learning Real-time Depth Map Fusion 论文地址：https://arxiv.org/pdf/2001.04388.pdf

更新

视觉常识R-CNN，Visual Commonsense R-CNN

https://arxiv.org/abs/2002.12204

Out-of-distribution图像检测

https://arxiv.org/abs/2002.11297

模糊视频帧插值，Blurry Video Frame Interpolation

https://arxiv.org/abs/2002.12259

元迁移学习零样本超分

https://arxiv.org/abs/2002.12213

3D室内场景理解

https://arxiv.org/abs/2002.12212

6.从有偏训练生成无偏场景图

https://arxiv.org/abs/2002.11949

自动编码双瓶颈哈希

https://arxiv.org/abs/2002.11930

一种用于人类轨迹预测的社会时空图卷积神经网络

https://arxiv.org/abs/2002.11927

面向面向深度人脸识别的通用表示学习

https://arxiv.org/abs/2002.11841

视觉表示泛化性

https://arxiv.org/abs/1912.03330

减弱上下文偏差

https://arxiv.org/abs/2002.11812

可迁移元技能的无监督强化学习

https://arxiv.org/abs/1911.07450

快速准确时空视频超分

https://arxiv.org/abs/2002.11616

对象关系图Teacher推荐学习的视频captioning

https://arxiv.org/abs/2002.11566

弱监督物体定位路由再思考

https://arxiv.org/abs/2002.11359

通过预培训学习视觉和语言导航的通用代理

https://arxiv.org/pdf/2002.10638.pdf

GhostNet轻量级神经网络

https://arxiv.org/pdf/1911.11907.pdf

AdderNet:在深度学习中，我们真的需要乘法吗?

https://arxiv.org/pdf/1912.13200.pdf

CARS:高效神经结构搜索的持续进化

https://arxiv.org/abs/1909.04977

通过协作式的迭代级联微调来移除单图像中的反射

https://arxiv.org/abs/1911.06634

深度神经网络的滤波嫁接

https://arxiv.org/pdf/2001.05868.pdf

PolarMask：将实例分割统一到FCN

https://arxiv.org/pdf/1909.13226.pdf

半监督语义图像分割

https://arxiv.org/pdf/1811.07073.pdf

通过选择性的特征再生来抵御通用攻击

https://arxiv.org/pdf/1906.03444.pdf

实时的基于细粒度草图的图像检索

https://arxiv.org/abs/2002.10310

用子问题询问VQA模型

https://arxiv.org/abs/1906.03444

从2D范例中学习神经三维纹理空间

https://geometry.cs.ucl.ac.uk/projects/2020/neuraltexture/

NestedVAE:通过薄弱的监督来隔离共同因素

https://arxiv.org/abs/2002.11576

实现多未来轨迹预测

https://arxiv.org/pdf/1912.06445.pdf

使用序列注意力模型进行稳健的图像分类

https://arxiv.org/pdf/1912.02184

原文连接 https://blog.csdn.net/qq_15698613/article/details/112469087