计算机视觉经典论文荟萃，深度学习方法占领9大方向，建议收藏

2017-12-20 专知 专知

【导读】近日，大连理工大学的学生ArcherFMY针对近几年深度学习在计算机视觉领域的应用提供了一个非常详细的阅读清单。如果你在深度学习领域是一个新手，你可以会想知道如何从哪篇论文开始阅读学习，如果你是从事计算机视觉领域，这一份详细的paper list，包括显著目标检测、视觉目标跟踪、目标检测、目标定位、语义分割和场景解析、边缘检测、姿态估计、超分辨率、图像分类，建议你收藏，仔细学习。本文转载已得到作者授权。


Github 地址：

https://github.com/ArcherFMY/Paper_Reading_List


Recommended Papers 

  • The goal of this document is to provide a reading list for Deep Learning in Computer Vision Field.

Paper Collections 

  • CVPR 2017 papers related to Attention Model

  • Paper List for Instance Aware Tasks

Topics Concerned

  • Salient Object Detection（显著目标检测）

  • Visual Object Tracking（视觉目标跟踪）

  • Object Detection（目标检测）

  • Object Localization（目标定位）

  • Semantic Segmentation and Scene Parsing（语义分割和场景解析）

  • Edge Detection（边缘检测）

  • Pose Estimation（姿态估计）

  • Super Resolution（超分辨率）

  • Image Classification（图像分类）

  • Others

Papers

Paper list.

  • Salient Object Detection（显著目标检测）

1. Visual Saliency Based on Multiscale Deep Features

Authors：Guanbin Li, Yizhou Yu

Pub：CVPR 2015

Links：https://sites.google.com/site/ligb86/mdfsaliency/




2. Saliency Detection by Multi-context Deep Learning

Authors：Rui Zhao, Wanli Ouyang, Hongsheng Li, Xiaogang Wang

Pub：CVPR 2015

Links：http://www.ee.cuhk.edu.hk/~rzhao/project/deepsal_cvpr15/zhaoOLWcvpr15.pdf

code:https://github.com/Robert0812/deepsaldet


3. Deep Networks for Saliency Detection via Local Estimation and Global Search


Authors：Lijun Wang, Huchuan Lu, Xiang Ruan, Ming-Hsuan Yang

Pub：CVPR 2015

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Deep_Networks_for_2015_CVPR_paper.pdf

code:https://drive.google.com/file/d/0B5rfGpkt3dDaVmhucE1jTVZGeTA/view


4. DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Authors：Nian Liu, Junwei Han

Pub：CVPR 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf

google drive：https://drive.google.com/file/d/0B1sbejbIJIW3RlJJY1NNNkFydEU/view

baiduyun：https://pan.ba 33 43790 33 14720 0 0 5957 0 0:00:07 0:00:02 0:00:05 5957idu.com/s/1jIm8cfk


5. Deep Contrast Learning for Salient Object Detection

Authors：Guanbin Li, Yizhou Yu

Pub：CVPR 2016

project page：http://i.cs.hku.hk/~gbli/deep_saliency.html


6. Saliency Unified: A Deep Architecture for Simultaneous Eye Fixation Prediction and Salient Object Segmentation


Authors：Srinivas S S Kruthiventi, Vennela Gudisa, Jaley H Dholakiya and R. Venkatesh Babu

Pub：CVPR 2016

project page：http://val.serc.iisc.ernet.in/saliency-unified/


7. Deep Saliency with Encoded Low level Distance Map and High Level Features

Authors：Gayoung Lee, Yu-Wing Tai, Junmo Kim

Pub：CVPR 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lee_Deep_Saliency_With_CVPR_2016_paper.pdf

code:https://github.com/gylee1103/SaliencyELD


8. Recurrent Attentional Networks for Saliency Detection

Authors：Jason Kuen, Zhenhua Wang, Gang Wang

Pub：CVPR 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Kuen_Recurrent_Attentional_Networks_CVPR_2016_paper.pdf


9. DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

Authors：Xi Li, Liming Zhao, Lina Wei, Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang

Pub：TIP 2016

Links：http://www.zhaoliming.net/research/deepsaliency


10. A Shape-Based Approach for Salient Object Detection Using Deep Learning

Authors：Jongpil Kim, Vladimir Pavlovic

Pub：ECCV 2016

Links：http://www.research.cs.rutgers.edu/~jpkim/papers/jpkim_eccv2016.pdf

Pre-computed Maps:http://www.research.cs.rutgers.edu/~jpkim/papers/resources/ssd_hs.tar.gz


11. Saliency Detection with Recurrent Fully Convolutional Networks

Authors：Linzhao Wang, Lijun Wang, Huchuan Lu, Pingping Zhang, Xiang Ruan

Pub：ECCV 2016

Links：https://www.researchgate.net/profile/Pingping_Zhang6/publication/308278832_Saliency_Detection_with_Recurrent_Fully_Convolutional_Networks/links/584b5da208aecb6bd8c157e0/Saliency-Detection-with-Recurrent-Fully-Convolutional-Networks.pdf

codes:https://drive.google.com/file/d/0B5rfGpkt3dDaODFRZ0ZXZjQyWDg/view


12. Deeply Supervised Salient Object Detection with Short Connections

Authors：Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip Torr

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.04849

github:https://github.com/Andrew-Qibin/DSS0


13. Non-Local Deep Features for Salient Object Detection

Authors：Zhiming Luo, Akshaya Mishra , Andrew Achkar , Justin Eichel , Shaozi Li , Pierre-Marc.Jodoin

Pub：CVPR 2017

Links：https://sites.google.com/view/zhimingluo/nldf


14. Instance-Level Salient Object Segmentation

Authors：Guanbin Li, Yuan Xie, Liang Lin, Yizhou Yu

Pub：CVPR 2017

Links：https://arxiv.org/pdf/1704.03604.pdf


15. Learning to Detect Salient Objects with Image-level Supervision

Authors：Lijun Wang, Huchuan Lu, Yifan Wang, Mengyang Feng, Dong Wang, Baocai Yin , Xiang Ruan

Pub：CVPR 2017

Links：http://saliencydetection.net/duts/download/camera_ready.pdf

github：https://github.com/scott89/WSS


16. Deep Level Sets for Salient Object Detection


Authors：Ping Hu, Bing Shuai, Jun Liu, Gang Wang

Pub：CVPR 2017

Links：http://openaccess.thecvf.com/content_cvpr_2017/papers/Hu_Deep_Level_Sets_CVPR_2017_paper.pdf


17. Learning Uncertain Convolutional Features for Accurate Saliency Detection

Authors：Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang, Baocai Yin

Pub：ICCV 2017

Links：https://arxiv.org/abs/1708.02031

github：https://github.com/Pchank/caffe-sal


18. Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

Authors：Pingping Zhang, Dong Wang, Huchuan Lu, Hongyu Wang, Xiang Ruan

Pub：ICCV 2017

Links：https://arxiv.org/abs/1708.02001

github：https://github.com/Pchank/caffe-sal


  • Visual Object Tracking（视觉目标跟踪）

Recommended Homepage---OTB Results. This shares results for more recent trackers.

https://github.com/foolwood/benchmark_results


  • Object Detection（目标检测）

1. Rich feature hierarchies for accurate object detection and semantic segmentation

Authors：Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik

Pub：CVPR 2014

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf

github：https://github.com/rbgirshick/rcnn


2. Fast R-CNN

Authors：Ross Girshick

Pub：ICCV 2015

Links：http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf

github：https://github.com/rbgirshick/fast-rcnn


3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Authors：Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

Pub：NIPS 2015

Links：http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

matlab：https://github.com/ShaoqingRen/faster_rcnn

python：https://github.com/rbgirshick/py-faster-rcnn

pytorch：https://github.com/longcw/faster_rcnn_pytorch


4. Convolutional Feature Masking for Joint Object and Stuff Segmentation

Authors：Jifeng Dai, Kaiming He, Jian Sun

Pub：CVPR 2015

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dai_Convolutional_Feature_Masking_2015_CVPR_paper.pdf


5. Instance-aware Semantic Segmentation via Multi-task Network Cascades

Authors：Jifeng Dai, Kaiming He, Jian Sun

Pub：CVPR 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.pdf

github：https://github.com/daijifeng001/MNC



6. R-FCN: Object Detection via Region-based Fully Convolutional Networks

Authors：Jifeng Dai, Yi Li, Kaiming He, Jian Sun

Pub：NIPS 2016

Links：https://arxiv.org/abs/1605.06409

github：https://github.com/daijifeng001/R-FCN


7. Feature Pyramid Networks for Object Detection

Authors：Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie

Pub：CVPR 2017

Links：https://arxiv.org/pdf/1612.03144.pdf


8. Mask R-CNN

Authors：Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick

Pub：ICCV 2017

Links：https://arxiv.org/abs/1703.06870


9. A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

Authors：Xiaolong Wang, Abhinav Shrivastava, Abhinav Gupta

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.03414

github：https://github.com/xiaolonw/adversarial-frcnn


10. Multiple Instance Detection Network with Online Instance Classifier Refinement

Authors：Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.00138


11. R-FCN-3000 at 30fps: Decoupling Detection and Classification

Authors：Bharat Singh, Hengdou Li, Abhishek Sharma and Larry S. Davis

Pub：Tech Report

Links：https://arxiv.org/abs/1712.01802


  • Object Localization（目标定位）


1. Simultaneous Detection and Segmentation

Authors：Bharath Hariharan, Pablo Arbeláez, Ross Girshick, Jitendra Malik

Pub：ECCV 2014

Links：https://arxiv.org/abs/1407.1808


2. Deep Self-Taught Learning for Weakly Supervised Object Localization


Authors：Zequn Jie, Yunchao Wei, Xiaojie Jin, Jiashi Feng, Wei Liu

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.05188


3. Learning Detection with Diverse Proposals

Authors：Samaneh Azadi, Jiashi Feng, Trevor Darrell

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.03533


4. Two-Phase Learning for Weakly Supervised Object Localization

Authors：Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon

Pub：ICCV 2017

Links：https://arxiv.org/abs/1708.02108


5. Soft Proposal Networks for Weakly Supervised Object Localization

Authors：Yi Zhu, Yanzhao Zhou, Qixiang Ye, Qiang Qiu and Jianbin Jiao

Pub：ICCV 2017

Links：https://arxiv.org/abs/1709.01829

github：https://github.com/ZhouYanzhao/SPN


  • Semantic Segmentation and Scene Parsing（语义分割和场景解析）


1. Fully Convolutional Networks for Semantic Segmentation

Authors：Jonathan Long, Evan Shelhamer, Trevor Darrell

Pub：CVPR 2015

Links：https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf


2. Learning to Segment Object Candidates

Authors：Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar

Pub：NIPS 2015

Links：http://papers.nips.cc/paper/5852-learning-to-segment-object-candidates.pdf


3. Learning to Refine Object Segments

Authors：Pedro O. Pinheiro , Tsung-Yi Lin , Ronan Collobert, Piotr Doll ́ar

Pub：arXiv 1603.08695

Links：https://arxiv.org/pdf/1603.08695.pdf


4. Conditional Random Fields as Recurrent Neural Networks

Authors：Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, ZhiZhong Su, Dalong Du, Chang Huang, and Philip H. S. Torr

Pub：ICCV 2015

Links：http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Zheng_Conditional_Random_Fields_ICCV_2015_paper.html


5. Learning Deconvolution Network for Semantic Segmentation

Authors：Heonwoo Noh, Seunghoon Hong, Bohyung Han

Pub：ICCV 2015

Links：http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Noh_Learning_Deconvolution_Network_ICCV_2015_paper.html


6. Instance-sensitive Fully Convolutional Networks

Authors：Jifeng Dai, Kaiming He, Yi Li, Shaoqing Ren, Jian Sun

Pub：ECCV 2016

Links：https://arxiv.org/abs/1603.08678


7. Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

Authors：Golnaz Ghiasi, Charless C. Fowlkes

Pub：ECCV 2016

Links：https://link.springer.com/chapter/10.1007/978-3-319-46487-9_32

github：https://github.com/golnazghiasi/LRR


8. Attention to Scale: Scale-aware Semantic Image Segmentation

Authors：Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu

Pub：CVPR 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Chen_Attention_to_Scale_CVPR_2016_paper.html

deeplab：http://liangchiehchen.com/projects/DeepLab.html


9. RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Authors：Guosheng Lin, Anton Milan, Chunhua Shen, Ian Reid

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.06612

github：https://github.com/guosheng/refinenet


10. Pyramid Scene Parsing Network

Authors：Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia

Pub：CVPR 2017

Links：https://arxiv.org/abs/1612.01105

github：https://github.com/hszhao/PSPNet


11. Dilated Residual Networks

Authors：Fisher Yu, Vladlen Koltun, Thomas Funkhouser

Pub：CVPR 2017

Links：https://arxiv.org/abs/1705.09914


12. Fully Convolutional Instance-aware Semantic Segmentation

Authors：Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, Yichen Wei

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.07709

github：https://github.com/msracver/FCIS


13. Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes


Authors：Tobias Pohlen, Alexander Hermans, Markus Mathias, Bastian Leibe

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.08323

github：https://github.com/TobyPDE/FRRN


14. Object Region Mining with Adversarial Erasing: A Simple Classification toSemantic Segmentation Approach

Authors：Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, Shuicheng Yan

Pub：CVPR 2017

Links：https://arxiv.org/abs/1703.08448


15. Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade


Authors：Xiaoxiao Li, Ziwei Liu, Ping Luo, Chen Change Loy, Xiaoou Tang

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.01344


16. Semantic Segmentation with Reverse Attention

Authors：Qin Huang, Chunyang Xia, Wuchi Hao, Siyang Li, Ye Wang, Yuhang Song and C.-C. Jay Kuo

Pub：BMVC 2017

Links：https://arxiv.org/abs/1707.06426

code：https://drive.google.com/drive/folders/0By2w_AaM8Rzbllnc3JCQjhHYnM?usp=sharing


17. Predicting Deeper into the Future of Semantic Segmentation

Authors：Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek and Yann LeCun

Pub：ICCV 2017

Links：https://arxiv.org/abs/1703.07684

project page：https://thoth.inrialpes.fr/people/pluc/iccv2017


18. Learning to Segment Every Thing

Authors：Ronghang Hu, Piotr Dollar, Kaiming He, Trevor Darrell, Ross Girshick

Pub：Tech Report

Links：https://arxiv.org/abs/1711.10370



  • Edge Detection（边缘检测）


1. Holistically-Nested Edge Detection

Authors：Saining Xie, Zhuowen Tu

Pub：ICCV 2015

Links：http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Xie_Holistically-Nested_Edge_Detection_ICCV_2015_paper.pdf

github：https://github.com/s9xie/hed


2. Richer Convolutional Features for Edge Detection

Authors：Yun Liu, Ming-Ming Cheng, Xiaowei Hu, Kai Wang, Xiang Bai

Pub：CVPR 2017

Links：https://arxiv.org/abs/1612.02103

project page：http://mmcheng.net/rcfedge/http://mmcheng.net/rcfedge/


3. CASENet: Deep Category-Aware Semantic Edge Detection


Authors：Zhiding Yu, Chen Feng, Ming-Yu Liu, Srikumar Ramalingam

Pub：CVPR 2017

Links：https://arxiv.org/abs/1705.09759



  • Pose Estimation（姿态估计）


1. Stacked Hourglass Networks for Human Pose Estimation

Authors：Alejandro Newell, Kaiyu Yang, and Jia Deng

Pub：ECCV 2016

Links：https://arxiv.org/abs/1603.06937


2. Multi-Context Attention for Human Pose Estimation

Authors：Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, Alan L. Yuille, Xiaogang Wang

Pub：CVPR 2017

Links：https://arxiv.org/abs/1702.07432

github：https://github.com/bearpaw/pose-attention


  • Super Resolution（超分辨率）

    

1. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Authors：Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang

Pub：CVPR 2017

project page：http://vllab1.ucmerced.edu/~wlai24/LapSRN/


2. Image Super-Resolution via Deep Recursive Residual Network

Authors：Ying Tai, Jian Yang, and Xiaoming Liu

Pub：CVPR 2017

Links：https://www.researchgate.net/profile/Xiaoming_Liu8/publication/316017318_Image_Super-Resolution_via_Deep_Recursive_Residual_Network/links/58eda40b0f7e9b37ed14f5d7/Image-Super-Resolution-via-Deep-Recursive-Residual-Network.pdf

github：https://github.com/tyshiwo/DRRN_CVPR17


  • Image Classification（图像分类）


1. Going Deeper with Convolutions


Authors：Ying Tai, Jian Yang, and Xiaoming Liu

Pub：CVPR 2015

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html

2. Deep Residual Learning for Image Recognition

Authors：Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun

Pub：CVPR 2016

Links：https://arxiv.org/abs/1512.03385

github：https://github.com/KaimingHe/deep-residual-networks


3. Residual Attention Network for Image Classification

Authors：Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.06904

github：https://github.com/buptwangfei/residual-attention-network


4. Aggregated Residual Transformations for Deep Neural Networks

Authors：Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.05431

github：https://github.com/facebookresearch/ResNeXt


5. Densely Connected Convolutional Networks

Authors：Gao Huang, Zhuang Liu, Kilian Q. Weinberger

Pub：CVPR 2017

Links：https://arxiv.org/abs/1608.06993

github：https://github.com/liuzhuang13/DenseNet


6. Deep Pyramidal Residual Networks


Authors：Dongyoon Han, Jiwhan Kim, Junmo Kim

Pub：CVPR 2017

Links：https://arxiv.org/pdf/1610.02915.pdf

github：https://github.com/jhkim89/PyramidNet



  • Others


1. Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

Authors：Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai

Pub：ICCV 2016

Links：http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Shen_Object_Skeleton_Extraction_CVPR_2016_paper.html

github：https://github.com/zeakey/DeepSkeleton


2. AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching

Authors：David Novotny, DianeLarlus, Andrea Vedaldi

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.04749


3. SRN：Side-output Residual Network for Object Symmetry Detection in the Wild

Authors：Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao and Qixiang Ye

Pub：CVPR 2017

Links：https://arxiv.org/abs/1703.02243

github：https://github.com/KevinKecc/SRN



4. Quality Aware Network for Set to Set Recognition

Authors：Yu Liu, Junjie Yan, Wanli Ouyang

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.03373


5. Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation


Authors：Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.02157

github：https://github.com/danxuhk/ContinuousCRF-CNN


6. Learning Cross-Modal Deep Representations for Robust Pedestrian Detectio

Authors：Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.02431


7. Semi-Supervised Deep Learning for Monocular Depth Map Prediction

Authors：Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe

Pub：CVPR 2017

Links：https://arxiv.org/abs/1702.02706


8. Detecting Visual Relationships with Deep Relational Networks

Authors：Bo Dai, Yuqi Zhang, Dahua Lin

Pub：CVPR 2017

Links：https://arxiv.org/pdf/1704.03114.pdf

github：https://github.com/doubledaibo/drnet


9. Annotating Object Instances with a Polygon-RNN

Authors：Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, Sanja Fidler

Pub：CVPR 2017

Links：https://arxiv.org/abs/1704.05548


10. Weakly Supervised Cascaded Convolutional Networks

Authors：Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash, Luc Van Gool

Pub：CVPR 2017

Links：https://arxiv.org/abs/1611.08258


11. Full Resolution Image Compression with Recurrent Neural Networks

Authors：George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell

Pub：CVPR 2017

Links：https://arxiv.org/abs/1608.05148

github：https://github.com/tensorflow/models/tree/master/compression


12. Few-Shot Object Recognition from Machine-Labeled Web Images


Authors：Zhongwen Xu, Linchao Zhu, Yi Yang

Pub：CVPR 2017

Links：https://arxiv.org/abs/1612.06152


13. UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory

Authors：Iasonas Kokkinos

Pub：CVPR 2017

Links：https://arxiv.org/abs/1609.02132

code：http://cvn.ecp.fr/ubernet/


14. Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

Authors：Vishwanath A. Sindagi and Vishal M. Patel

Pub：ICCV 2017

Links：https://arxiv.org/abs/1708.00953


15. MemNet: A Persistent Memory Network for Image Restoration


Authors：Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu

Pub：ICCV 2017

Links：https://arxiv.org/abs/1708.02209

githib：https://github.com/tyshiwo/MemNet


16. Data Distillation: Towards Omni-Supervised Learning


Authors：Ilija Radosavovic, Piotr Dollar, Ross Girshick, GeorgiaGkioxari and Kaiming He

Pub：Tech Report

Links：https://arxiv.org/abs/1712.04440


17. Non-local Neural Networks

Authors：Xiaolong Wang, Ross Girshick, Abhinav Gupta and Kaiming He

Pub：Tech Report

Links：https://arxiv.org/abs/1711.07971


参考链接：

https://github.com/ArcherFMY/Paper_Reading_List

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