目标检测经典论文集锦
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说实话，单是CVPR2019就有1300篇文章了，还有ECCV，ICCV，AAAI，ICLR，NeurlPS，BMVC，TPAMI，IJCV，ECML-PKDD，还有预印本的arXiv，是不是光会议就看花了眼？这么多文章是不可能全都看的，这时候就需要挑一些高质量的论文拿出来看看。但是如何找出高质量论文也是一件棘手和费时的问题，不妨看看这些个大佬的总结。
下面这张图来自github 5000多star的项目：deep learning object detection
该项目总结了从2014到2019年各大会议的优秀文章，并将重量级论文标红为必读，当然此处未标红的论文也很重要，可以在时间充足的情况下阅读。
github链接：
部分论文性能对比
这里给出的是mAP的比较。没有给出FPS的比较，因为每篇论文的作者给出的FPS都是基于不同的硬件，直接对比没有太大意义。
历年经典论文
2014
-
[R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR’ 14] |
[pdf][official code - caffe] -
[OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR’ 14] |
[pdf][official code - torch] -
[MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR’ 14] |
[pdf] -
[SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV’ 14] |
[pdf][official code - caffe][unofficial code - keras][unofficial code - tensorflow]
2015
-
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR’ 15] |
[pdf][official code - matlab] -
[MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV’ 15] |
[pdf][official code - caffe] -
[DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV’ 15] |
[pdf][official code - caffe] -
[AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV’ 15] |
[pdf] -
[Fast R-CNN] Fast R-CNN | [ICCV’ 15] |
[pdf][official code - caffe] -
[DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV’ 15] |
[pdf][official code - matconvnet] -
[Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS’ 15] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch]
2016
-
[YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR’ 16] |
[pdf][official code - c] -
[G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR’ 16] |
[pdf] -
[AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR’ 16] |
[pdf] -
[ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR’ 16] |
[pdf] -
[HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR’ 16] |
[pdf] -
[OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR’ 16] |
[pdf][official code - caffe] -
[CRAPF] CRAFT Objects from Images | [CVPR’ 16] |
[pdf][official code - caffe] -
[MPN] A MultiPath Network for Object Detection | [BMVC’ 16] |
[pdf][official code - torch] -
[SSD] SSD: Single Shot MultiBox Detector | [ECCV’ 16] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch] -
[GBDNet] Crafting GBD-Net for Object Detection | [ECCV’ 16] |
[pdf][official code - caffe] -
[CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV’ 16] |
[pdf] -
[MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV’ 16] |
[pdf][official code - caffe] -
[R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS’ 16] |
[pdf][official code - caffe][unofficial code - caffe] -
[PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW’ 16] |
[pdf][official code - caffe] -
[DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI’ 16] |
[pdf] -
[NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI’ 16] |
[pdf]
2017
-
[DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv’ 17] |
[pdf][official code - caffe] -
[TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR’ 17] |
[pdf] -
[FPN] Feature Pyramid Networks for Object Detection | [CVPR’ 17] |
[pdf][unofficial code - caffe] -
[YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR’ 17] |
[pdf][official code - c][unofficial code - caffe][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch] -
[RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR’ 17] |
[pdf][official code - caffe][unofficial code - tensorflow] -
[RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV’ 17] |
[pdf][official code - caffe] -
[DCN] Deformable Convolutional Networks | [ICCV’ 17] |
[pdf][official code - mxnet][unofficial code - tensorflow][unofficial code - pytorch] -
[DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV’ 17] |
[pdf][official code - theano] -
[CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV’ 17] |
[pdf][official code - caffe] -
[RetinaNet] Focal Loss for Dense Object Detection | [ICCV’ 17] |
[pdf][official code - keras][unofficial code - pytorch][unofficial code - mxnet][unofficial code - tensorflow] -
[Mask R-CNN] Mask R-CNN | [ICCV’ 17] |
[pdf][official code - caffe2][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch] -
[DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV’ 17] |
[pdf][official code - caffe][unofficial code - pytorch] -
[SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV’ 17] |
[pdf] -
[Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv’ 17] |
[pdf][official code - tensorflow] -
[Soft-NMS] Improving Object Detection With One Line of Code | [ICCV’ 17] |
[pdf][official code - caffe]
2018
-
[YOLO v3] YOLOv3: An Incremental Improvement | [arXiv’ 18] |
[pdf][official code - c][unofficial code - pytorch][unofficial code - pytorch][unofficial code - keras][unofficial code - tensorflow] -
[ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV’ 18] |
[pdf][official code - caffe] -
[SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR’ 18] |
[pdf][official code - tensorflow] -
[STDN] Scale-Transferrable Object Detection | [CVPR’ 18] |
[pdf] -
[RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR’ 18] |
[pdf][official code - caffe][unofficial code - chainer][unofficial code - pytorch] -
[MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR’ 18] |
[pdf] -
[DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR’ 18] |
[pdf][official code - caffe] -
[SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR’ 18] |
[pdf] -
[Relation-Network] Relation Networks for Object Detection | [CVPR’ 18] |
[pdf][official code - mxnet] -
[Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR’ 18] |
[pdf][official code - caffe] -
Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR’ 18] |
[pdf] -
[MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR’ 18] |
[pdf][official code - caffe] -
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR’ 18] |
[pdf][official code - chainer] -
[Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR’ 18] |
[pdf] -
[STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC’ 18] |
[pdf] -
[RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV’ 18] |
[pdf][official code - pytorch] -
Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV’ 18] |
[pdf] -
[CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV’ 18] |
[pdf][official code - pytorch] -
[PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV’ 18] |
[pdf] -
[Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv’ 18] |
[pdf] -
[ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD’ 18] |
[pdf][official code - tensorflow] -
[Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS’ 18] |
[pdf][official code - caffe] -
[HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS’ 18] |
[pdf] -
[MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS’ 18] |
[pdf] -
[SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS’ 18] |
[pdf]
2019
-
[M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI’ 19] |
[pdf][official code - pytorch] -
[R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI’ 19] |
[pdf] -
[CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR’ 19] |
[pdf] -
Feature Intertwiner for Object Detection | [ICLR’ 19] |
[pdf] -
[GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR’ 19] |
[pdf] -
Automatic adaptation of object detectors to new domains using self-training | [CVPR’ 19] |
[pdf] -
[Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR’ 19] |
[pdf] -
Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR’ 19] |
[pdf] -
[ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR’ 19] |
[pdf]|[official code - pytorch] -
[C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
| [CVPR’ 19] |[pdf]|[official code - torch] -
[ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR’ 19] |
[pdf] -
Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR’ 19] |
[pdf]|[official code - caffe2] -
Activity Driven Weakly Supervised Object Detection | [CVPR’ 19] |
[pdf] -
Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR’ 19] |
[pdf] -
Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR’ 19] |
[pdf]|[official code - pytorch] -
[NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR’ 19] |
[pdf] -
[Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR’ 19] |
[pdf] -
Point in, Box out: Beyond Counting Persons in Crowds | [CVPR’ 19] |
[pdf] -
Locating Objects Without Bounding Boxes | [CVPR’ 19] |
[pdf] -
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR’ 19] |
[pdf] -
Towards Universal Object Detection by Domain Attention | [CVPR’ 19] |
[pdf] -
Exploring the Bounds of the Utility of Context for Object Detection | [CVPR’ 19] |
[pdf] -
What Object Should I Use? - Task Driven Object Detection | [CVPR’ 19] |
[pdf] -
Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR’ 19] |
[pdf] -
Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR’ 19] |
[pdf] -
Fully Quantized Network for Object Detection | [CVPR’ 19] |
[pdf] -
Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR’ 19] |
[pdf] -
Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR’ 19] |
[pdf] -
[Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR’ 19] |
[pdf] -
Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR’ 19] |
[pdf] -
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR’ 19] |
[pdf] -
Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR’ 19] |
[pdf] -
[MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR’ 19] |
[pdf] -
You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR’ 19] |
[pdf] -
Object detection with location-aware deformable convolution and backward attention filtering | [CVPR’ 19] |
[pdf] -
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR’ 19] |
[pdf]
参考：
https://github.com/hoya012/deep_learning_object_detection
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标签：code,检测,Object,Detection,CVPR,经典,pdf,论文集,official 来源： https://blog.csdn.net/u010552731/article/details/95385187