Cvpr2020 | the latest cvpr2020 papers are the first to see, with all download links attached!

Continuous updating Github：

https://github.com/Sophia-11/Awesome-CVPR-Paper 

2021 Baidu cloud, please go to 【 The computer vision Alliance 】 The background to reply  CVPR2021

A collection of previous years' papers can be found in 【 The computer vision Alliance 】 The background to reply  CVPR2019 

2020 For a collection of continuing papers, please go to 【 The computer vision Alliance 】 The background to reply  CVPR2020

---

CVPR 2021 The 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/

---

CVPR 2020

Dedicated to computer vision and pattern recognition including color detection 、 track 、 motion 、 Object recognition 、 Sound and object detection .

CVPR 2020 Topics covered :

• Segmentation and grouping
• Movement and tracking
• Human cognition
• Shape-from-X
• Sound and structure and motion
• Color and texture
• Lighting and reflection modeling
• Image based modeling
• sensor
• Shape representation and matching
• Calculate photography and video
• Early and biologically inspired visions
• Video analysis and event recognition
• An optimization method
• Face and posture analysis
• Video surveillance
• On site understanding
• Image and video retrieval
• Medical image analysis
• Vision for robots
• Vision for graphics
• Statistical methods and learning
• The application of computer vision
• Document analysis
• Object recognition / testing / classification

CVPR--IEEE Conference on Computer Vision and Pattern Recognition

Location: United States

Date: June 2020

Deadline: TBD, about Nov

CVPR 2020 Will be held in Seattle , Time is 6 month 13-19 Japan .

The president of the meeting is Gerard Medioni（ University of Southern California & Head of Amazon Research ,Amazon Go）,Ramin Zabih（ Cornell University ） and Terry Boult（ Colorado Springs ）; The program chairman is liuce （ Google & MIT）、Greg Mori（ Simon Fraser University in Canada ）,Kate Saenko（ Boston university ） and Silvio Savarese（ Stanford university ）.

among , Liu CE, chairman of the Chinese program Tsinghua University Graduate School , stay MIT Get a doctorate （CSAIL）,2014 Joined Google in , Now he is a senior research scientist . Except as CVPR 2020 Chairman of the joint procedure , Liu CE is still there CVPR 2017 and ICCV 2015 Chair the field .

  Thesis recruitment list

Thesis acceptance list ：

http://cvpr2020.thecvf.com/sites/default/files/2020-02/accepted_list.txt

One of the top three computer vision conferences CVPR2020 The results of the reception have been announced , Altogether 1470 Papers were received , The receiving rate is 22%, Less than last year 3 percentage , The competition is more and more fierce .

 

Computer vision will CVPR2020 The official list of accepted papers was released today （ Number ）：

http://cvpr2020.thecvf.com/sites/default/files/2020-02/accepted_list.txt

 

 

Although we can only see the official received papers ID, The specific receiving papers are not clear , but Some of the authors have shown what they have received , Some have been released Open source code . therefore , After the efforts of Xiaobian , We have collected a number of papers and introductions from the authors （ Because there is no official notice , If there is a mistake , Welcome to point out ）, The list is as follows , You can read it first .

1.  Visual common sense R-CNN,Visual Commonsense R-CNN

https://arxiv.org/abs/2002.12204

 

2. Out-of-distribution Image detection

https://arxiv.org/abs/2002.11297

 

3.  Blur video frame interpolation ,Blurry Video Frame Interpolation

https://arxiv.org/abs/2002.12259

 

4.  Metamigration learning zero sample over score

https://arxiv.org/abs/2002.12213

 

5. 3D Indoor scene understanding

https://arxiv.org/abs/2002.12212

 

 

6. Generate unbiased scene graph from biased training

https://arxiv.org/abs/2002.11949

 

 

7.  Auto Code double bottleneck hash

https://arxiv.org/abs/2002.11930

 

8.  A convolution neural network for the prediction of human trajectories

https://arxiv.org/abs/2002.11927

 

9.  General representation learning for deep face recognition

https://arxiv.org/abs/2002.11841

 

10.  Visual representation generalization

https://arxiv.org/abs/1912.03330

11.  Reduce context bias

https://arxiv.org/abs/2002.11812

 

12.  Unsupervised reinforcement learning of transferable metaskills

https://arxiv.org/abs/1911.07450

 

13.  Fast and accurate spatiotemporal video super division

https://arxiv.org/abs/2002.11616

 

14.  Object graph Teacher Recommended learning videos captioning

https://arxiv.org/abs/2002.11566

 

15.  Rethinking the location route of weakly supervised objects

https://arxiv.org/abs/2002.11359

 

16.  Learn general agent of vision and language navigation through pre training

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

 

17. GhostNet Lightweight neural network

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

 

 

18. AdderNet: In deep learning , Do we really need multiplication ?

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

 

19. CARS: The continuous evolution of efficient neural structure search

https://arxiv.org/abs/1909.04977

 

20.  Remove reflections from a single image through collaborative iterative cascading fine-tuning

https://arxiv.org/abs/1911.06634

 

https://github.com/Sophia-11/Awesome-CVPR-Paper 

A collection of previous years' papers can be found in 【 The computer vision Alliance 】 The background to reply  CVPR2019 

2020 For a collection of continuing papers, please go to 【 The computer vision Alliance 】 The background to reply  CVPR2020

cvpr2019 Paper download ：

CVPR2019| newest CVPR2019 Read the paper first , All download links are attached ！