目标检测经典论文集锦

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目标检测经典论文集锦

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:

https://github.com/hoya012/deep_learning_object_detection

目标检测经典论文集锦


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。
目标检测经典论文集锦

历年经典论文

2014

2015

2016

2017

2018

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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