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计算机视觉各个方向论文速览
https://github.com/Sophia-11/Awesome-CV-Paper-Review
List: Awesome-CV-Paper-Review
artificial-intelligence computer-vision cvpr deep-learning eccv iccv machine-learning
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计算机视觉各个方向论文速览
Host: GitHub
URL: https://github.com/Sophia-11/Awesome-CV-Paper-Review
Owner: Sophia-11
Created: 2019-07-07T05:38:42.000Z (about 5 years ago)
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Last Pushed: 2019-08-30T10:01:50.000Z (about 5 years ago)
Last Synced: 2024-05-20T05:11:14.818Z (4 months ago)
Topics: artificial-intelligence, computer-vision, cvpr, deep-learning, eccv, iccv, machine-learning
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- Readme: README.md
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README

        # 计算机视觉各个方向论文速览

*Last updated: 2019/08/30*

#### Update log

* 2019/08/30 * - 

## Table of Contents

- [目标检测](https://github.com/Sophia-11/Awesome-CV-Paper-Review/edit/master/README.md##目标检测论文分类)

# Top Papers of Object Detection

## 2019 CVPR of Object Detection

- [Feature Selective Anchor-Free Module for Single-Shot Object Detection ](https://arxiv.org/pdf/1903.00621.pdf) 

- [ Bottom-up Object Detection by Grouping Extreme and Center Points ](https://arxiv.org/pdf/1901.08043.pdf)[` [pytorch]`](https://github.com/xingyizhou/ExtremeNet)

- [C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection

 ](https://arxiv.org/pdf/1904.05647.pdf)[`[ torch]`](https://github.com/AnonymousIDs/C-MIL)

- [ MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Cai_MaxpoolNMS_Getting_Rid_of_NMS_Bottlenecks_in_Two-Stage_Object_Detectors_CVPR_2019_paper.pdf)

- [You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection ](https://web.cs.ucdavis.edu/~yjlee/projects/cvpr2019-youreapwhatyousow.pdf)

- [Object detection with location-aware deformable convolution and backward attention filtering ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Object_Detection_With_Location-Aware_Deformable_Convolution_and_Backward_Attention_Filtering_CVPR_2019_paper.pdf)

- [ ScratchDet: Training Single-Shot Object Detectors from Scratch ](https://arxiv.org/pdf/1810.08425.pdf) 

- [Bounding Box Regression with Uncertainty for Accurate Object Detection ](https://arxiv.org/pdf/1809.08545.pdf) [`[caffe2]`](https://github.com/yihui-he/KL-Loss)

- [Activity Driven Weakly Supervised Object Detection ](https://arxiv.org/pdf/1904.01665.pdf) 

- [Towards Accurate One-Stage Object Detection with AP-Loss ](https://arxiv.org/pdf/1904.06373.pdf) 

- [Strong-Weak Distribution Alignment for Adaptive Object Detection ](https://arxiv.org/pdf/1812.04798.pdf)[`[pytorch]`](https://github.com/VisionLearningGroup/DA_Detection) 

- [ NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection ](https://arxiv.org/pdf/1904.07392.pdf) 

- [Adaptive NMS: Refining Pedestrian Detection in a Crowd ](https://arxiv.org/pdf/1904.03629.pdf) 

- [Point in, Box out: Beyond Counting Persons in Crowds ](https://arxiv.org/pdf/1904.01333.pdf) 

- [Locating Objects Without Bounding Boxes ](https://arxiv.org/pdf/1806.07564.pdf) 

- [Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects ](https://arxiv.org/pdf/1811.10862.pdf) 

- [Towards Universal Object Detection by Domain Attention ](https://arxiv.org/pdf/1904.04402.pdf) 

- [Exploring the Bounds of the Utility of Context for Object Detection ](https://arxiv.org/pdf/1711.05471.pdf) 

- [What Object Should I Use? Task Driven Object Detection ](https://arxiv.org/pdf/1904.03000.pdf) 

- [Dissimilarity Coefficient based Weakly Supervised Object Detection ](https://arxiv.org/pdf/1811.10016) 

- [Adapting Object Detectors via Selective Cross-Domain Alignment ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Adapting_Object_Detectors_via_Selective_Cross-Domain_Alignment_CVPR_2019_paper.pdf) 

- [Fully Quantized Network for Object Detection ](https://yan-junjie.github.io/publication/dblp-confcvprlilqwfy-19/dblp-confcvprlilqwfy-19.pdf)

- [Distilling Object Detectors with Fine-grained Feature Imitation ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Distilling_Object_Detectors_With_Fine-Grained_Feature_Imitation_CVPR_2019_paper.pdf)

- [Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_Multi-Task_Self-Supervised_Object_Detection_via_Recycling_of_Bounding_Box_Annotations_CVPR_2019_paper.pdf)

- [ Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection ]()

- [ Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression ](https://arxiv.org/pdf/1902.09630.pdf) 

- [Automatic adaptation of object detectors to new domains using self-training ](https://arxiv.org/pdf/1904.07305.pdf) 

- [ Libra R-CNN: Balanced Learning for Object Detection ](https://arxiv.org/pdf/1904.02701.pdf) 

- [Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation ](https://arxiv.org/pdf/1905.05980.pdf)

- [Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors ](https://pdfs.semanticscholar.org/ec96/b6ae95e1ebbe4f7c0252301ede26dfc79467.pdf)

- [Spatial-aware Graph Relation Network for Large-scale Object Detection ](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Spatial-Aware_Graph_Relation_Network_for_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)

- [Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection ](https://arxiv.org/pdf/1905.05396.pdf)

## 2019 ICLR of Object Detection

- [CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild ](https://openreview.net/pdf?id=SJgEl3A5tm) 

- [Feature Intertwiner for Object Detection](https://openreview.net/pdf?id=SyxZJn05YX) 

## 2019 AAAI of Object Detection

- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://arxiv.org/pdf/1811.04533.pdf)  [`[pytorch]`](https://github.com/qijiezhao/M2Det)

- [Object Detection based on Region Decomposition and Assembly](https://arxiv.org/pdf/1901.08225v1.pdf) 

## 2018 CVPR of Object Detection

- **[DA Faster R-CNN]**[Domain Adaptive Faster R-CNN for Object Detection in the Wild](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.pdf) [`[caffe]`](https://github.com/yuhuayc/da-faster-rcnn)

- **[SNIP]**[An Analysis of Scale Invariance in Object Detection – SNIP](https://arxiv.org/pdf/1711.08189.pdf)

- **[Relation-Network]**[Relation Networks for Object Detection](https://arxiv.org/pdf/1711.11575.pdf) [`[mxnet]`](https://github.com/msracver/Relation-Networks-for-Object-Detection)

- **[Cascade R-CNN]**[Cascade R-CNN: Delving into High Quality Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cai_Cascade_R-CNN_Delving_CVPR_2018_paper.pdf) [`[caffe]`](https://github.com/zhaoweicai/cascade-rcnn)

- **[SIN]**[Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Structure_Inference_Net_CVPR_2018_paper.pdf) [`[tensorflow]`](https://github.com/choasup/SIN)

- **[STDN]**[Scale-Transferrable Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.pdf)

- **[RefineDet]**[Single-Shot Refinement Neural Network for Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf) [`[caffe]`](https://github.com/sfzhang15/RefineDet)

- **[MegDet]**[MegDet: A Large Mini-Batch Object Detector](http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_MegDet_A_Large_CVPR_2018_paper.pdf)

- [Finding Tiny Faces in the Wild with Generative Adversarial Network](https://ivul.kaust.edu.sa/Documents/Publications/2018/Finding%20Tiny%20Faces%20in%20the%20Wild%20with%20Generative%20Adversarial%20Network.pdf)

- **[MLKP]**[Multi-scale Location-aware Kernel Representation for Object Detection](https://arxiv.org/pdf/1804.00428.pdf) [`[caffe]`](https://github.com/Hwang64/MLKP)

- [Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation](https://arxiv.org/pdf/1803.11365.pdf) [`[chainer]`](https://github.com/naoto0804/cross-domain-detection)

- **[Fitness NMS]**[Improving Object Localization with Fitness NMS and Bounded IoU Loss](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0794.pdf) 

## 2018 ECCV of Object Detection

- **[RFBNet]**[Receptive Field Block Net for Accurate and Fast Object Detection](https://arxiv.org/pdf/1711.07767.pdf) [`[pytorch]`](https://github.com/ruinmessi/RFBNet)

- [Zero-Annotation Object Detection with Web Knowledge Transfer](http://openaccess.thecvf.com/content_ECCV_2018/papers/Qingyi_Tao_Zero-Annotation_Object_Detection_ECCV_2018_paper.pdf)

- **[CornerNet]**[CornerNet: Detecting Objects as Paired Keypoints](https://arxiv.org/pdf/1808.01244.pdf) [`[pytorch]`](https://github.com/princeton-vl/CornerNet)

- **[PFPNet]**[Parallel Feature Pyramid Network for Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Seung-Wook_Kim_Parallel_Feature_Pyramid_ECCV_2018_paper.pdf)

## 2018 NIPS of Object Detection

- **[Pelee]**[Pelee: A Real-Time Object Detection System on Mobile Devices](http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices.pdf) [`[caffe]`](https://github.com/Robert-JunWang/Pelee)

- **[HKRM]**[Hybrid Knowledge Routed Modules for Large-scale Object Detection](http://papers.nips.cc/paper/7428-hybrid-knowledge-routed-modules-for-large-scale-object-detection.pdf) 

- **[MetaAnchor]**[MetaAnchor: Learning to Detect Objects with Customized Anchors](http://papers.nips.cc/paper/7315-metaanchor-learning-to-detect-objects-with-customized-anchors.pdf) 

- **[SNIPER]**[SNIPER: Efficient Multi-Scale Training](http://papers.nips.cc/paper/8143-sniper-efficient-multi-scale-training.pdf)

## 2017 CVPR of Object Detection

- **[DSSD]**[DSSD : Deconvolutional Single Shot Detector](https://arxiv.org/pdf/1701.06659.pdf) [`[caffe]`](https://github.com/chengyangfu/caffe/tree/dssd)

- **[TDM]**[Beyond Skip Connections: Top-Down Modulation for Object Detection](https://arxiv.org/pdf/1612.06851.pdf)

- **[FPN]**[Feature Pyramid Networks for Object Detection ](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf) 

- **[YOLO v2]**[YOLO9000: Better, Faster, Stronger](https://arxiv.org/pdf/1612.08242.pdf) [`[c]`](https://pjreddie.com/darknet/yolo/) 

- **[RON]**[RON: Reverse Connection with Objectness Prior Networks for Object Detection](https://arxiv.org/pdf/1707.01691.pdf) [`[caffe]`](https://github.com/taokong/RON) 

## 2017 ICCV of Object Detection

- **[DeNet]**[DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling](https://arxiv.org/pdf/1703.10295.pdf) [`[theano]`](https://github.com/lachlants/denet)

- **[CoupleNet]**[CoupleNet: Coupling Global Structure with Local Parts for Object Detection](https://arxiv.org/pdf/1708.02863.pdf) [`[caffe]`](https://github.com/tshizys/CoupleNet)

- **[RetinaNet]**[Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf) [`[keras]`](https://github.com/fizyr/keras-retinanet)

- **[Mask R-CNN]**[Mask R-CNN](http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf) [`[caffe2]`](https://github.com/facebookresearch/Detectron) 

- **[RSA]**[Recurrent Scale Approximation for Object Detection in CNN | ](https://arxiv.org/pdf/1707.09531.pdf) [`[caffe]`](https://github.com/sciencefans/RSA-for-object-detection)

- **[DCN]**[Deformable Convolutional Networks ](http://openaccess.thecvf.com/content_ICCV_2017/papers/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.pdf) [`[mxnet]`](https://github.com/msracver/Deformable-ConvNets) 

- **[DSOD]**[DSOD: Learning Deeply Supervised Object Detectors from Scratch](https://arxiv.org/pdf/1708.01241.pdf) [`[caffe]`](https://github.com/szq0214/DSOD) 

- **[SMN]**[Spatial Memory for Context Reasoning in Object Detection](http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Spatial_Memory_for_ICCV_2017_paper.pdf)

- **[Light-Head R-CNN]**[Light-Head R-CNN: In Defense of Two-Stage Object Detector](https://arxiv.org/pdf/1711.07264.pdf) [`[tensorflow]`](https://github.com/zengarden/light_head_rcnn)

- **[Soft-NMS]**[Improving Object Detection With One Line of Code](https://arxiv.org/pdf/1704.04503.pdf) [`[caffe]`](https://github.com/bharatsingh430/soft-nms)

## 2016 CVPR of Object Detection

- **[YOLO v1]**[You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/pdf/1506.02640.pdf) [`[c]`](https://pjreddie.com/darknet/yolo/) 

- **[G-CNN]**[G-CNN: an Iterative Grid Based Object Detector](https://arxiv.org/pdf/1512.07729.pdf)

- **[AZNet]**[Adaptive Object Detection Using Adjacency and Zoom Prediction](https://arxiv.org/pdf/1512.07711.pdf)

- **[ION]**[Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks](https://arxiv.org/pdf/1512.04143.pdf)

- **[HyperNet]**[HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection](https://arxiv.org/pdf/1604.00600.pdf)

- **[OHEM]**[Training Region-based Object Detectors with Online Hard Example Mining](https://arxiv.org/pdf/1604.03540.pdf) [`[caffe]`](https://github.com/abhi2610/ohem) 

- **[CRAPF]**[CRAFT Objects from Images](https://arxiv.org/pdf/1604.03239.pdf) [`[caffe]`](https://github.com/byangderek/CRAFT) 

## 2016 ECCV of Object Detection

- **[SSD]**[SSD: Single Shot MultiBox Detector](https://arxiv.org/pdf/1512.02325.pdf) [`[caffe]`](https://github.com/weiliu89/caffe/tree/ssd)

- **[GBDNet]**[Crafting GBD-Net for Object Detection](https://arxiv.org/pdf/1610.02579.pdf) [`[caffe]`](https://github.com/craftGBD/craftGBD)

- **[CPF]**[Contextual Priming and Feedback for Faster R-CNN](https://pdfs.semanticscholar.org/40e7/4473cb82231559cbaeaa44989e9bbfe7ec3f.pdf)

- **[MS-CNN]**[A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection](https://arxiv.org/pdf/1607.07155.pdf) [`[caffe]`](https://github.com/zhaoweicai/mscnn)

## 2016 NIP and PAMI of Object Detection

- **[R-FCN]** **[NIPS' 16]**[R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://arxiv.org/pdf/1605.06409.pdf) [`[caffe]`](https://github.com/daijifeng001/R-FCN)

- **[PVANET]****[NIPSW' 16]**[PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection](https://arxiv.org/pdf/1608.08021.pdf) [`[caffe]`](https://github.com/sanghoon/pva-faster-rcnn)

- **[DeepID-Net]****[PAMI' 16]**[DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection](https://arxiv.org/pdf/1412.5661.pdf)

- **[NoC]****[TPAMI' 16]**[Object Detection Networks on Convolutional Feature Maps](https://arxiv.org/pdf/1504.06066.pdf)

## 2015 CVPR and NIPS of Object Detection

- [Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction](https://arxiv.org/pdf/1504.03293.pdf) [`[matlab]`](https://github.com/YutingZhang/fgs-obj)

- **[Faster R-CNN, RPN]****[NIPS' 15]**[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)  [`[caffe]`](https://github.com/rbgirshick/py-faster-rcnn)

## 2015 ICCV of Object Detection

- **[MR-CNN]**[Object detection via a multi-region & semantic segmentation-aware CNN model](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf) [`[caffe]`](https://github.com/gidariss/mrcnn-object-detection)

- **[DeepBox]**[DeepBox: Learning Objectness with Convolutional Networks](https://arxiv.org/pdf/1505.02146.pdf) [`[caffe]`](https://github.com/weichengkuo/DeepBox)

- **[AttentionNet]**[AttentionNet: Aggregating Weak Directions for Accurate Object Detection](https://arxiv.org/pdf/1506.07704.pdf) 

- **[Fast R-CNN]**[Fast R-CNN](https://arxiv.org/pdf/1504.08083.pdf) [`[caffe]`](https://github.com/rbgirshick/fast-rcnn) 

- **[DeepProposal]**[DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers](https://arxiv.org/pdf/1510.04445.pdf)  [`[matconvnet]`](https://github.com/aghodrati/deepproposal)

## 2014 CVPR & ICLR & ECCV of Object Detection

- **[R-CNN]**[Rich feature hierarchies for accurate object detection and semantic segmentation](https://arxiv.org/pdf/1311.2524.pdf) [`[caffe]`](https://github.com/rbgirshick/rcnn) 

- **[OverFeat]****[ICLR' 14]**[OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://arxiv.org/pdf/1312.6229.pdf) [`[torch]`](https://github.com/sermanet/OverFeat) 

- **[MultiBox]**[Scalable Object Detection using Deep Neural Networks](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf)

- **[SPP-Net]****[ECCV' 14]**[Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729.pdf) [`[caffe]`](https://github.com/ShaoqingRen/SPP_net)

# Image Classification

## 2019-07-07

* [Multi-Instance Multi-Scale CNN for Medical Image Classification](https://github.com/Sophia-11/Awesome-CV-Paper-Review/blob/master/Image%20Classification/Multi-Instance%20Multi-Scale%20CNN%20for%20Medical.md)

# Semantic Segmentation

## 2019-07-08

* [Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation](https://github.com/Sophia-11/Awesome-CV-Paper-Review/blob/master/Semantic%20Segmentation/2019-07-07/Proposal%2C%20Tracking%20and%20Segmentation%20(PTS)%20A%20Cascaded%20Network%20for%20Video%20Object%20Segmentation.md)

* [FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation ](http://wuhuikai.me/FastFCNProject/fast_fcn.pdf)[`[paper]`](https://arxiv.org/abs/1903.11816)[`[code]`](https://github.com/wuhuikai/FastFCN)

# Object Detection

## 2019-07-09

* [Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos](https://github.com/Sophia-11/Awesome-CV-Paper-Review/blob/master/Object%20Detection/2019-07-09/Multi-Cue%20Vehicle%20Detection%20for%20Semantic%20Video%20Compression%20In%20Georegistered%20Aerial%20Videos.md)