2019 | Anchor-free的对象检测网络汇总

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论文

从去年第开始，在深度学习的对象检测技术方向，刮起了一阵anchor-free网络模型的旋风，涌现出了很多anchor-free的对象检测网络模型。相关论文收集整理，汇总如下方便大家查阅：

• CornerNet
  CornerNet: Keypoint Triplets for Object Detection
  https://arxiv.org/pdf/1808.01244.pdf

• ExtremeNet
  Bottom-up Object Detection by Grouping Extreme and Center Points
  https://arxiv.org/pdf/1901.08043.pdf

• CornerNet-Lite
  CornerNet-Lite: Efficient Keypoint Based Object Detection
  https://arxiv.org/pdf/1904.08900.pdf

• Segmentations is All You Need
  https://arxiv.org/pdf/1904.13300.pdf

• FCOS
  Fully Convolutional One-Stage Object Detection
  https://arxiv.org/abs/1904.01355.pdf

• Fovea|FoveaBox: 
  Beyond Anchor-based Object Detector
  https://arxiv.org/pdf/1904.03797.pdf

• CenterNet^1
  Objects as Points
  https://arxiv.org/pdf/1904.07850.pdf

• CenterNet^2
  CenterNet: Keypoint Triplets for Object Detection
  https://arxiv.org/pdf/1904.08189.pdf

• DuBox|DuBox: 
  No-Prior Box Objection Detection via Residual Dual Scale Detectors
  https://arxiv.org/pdf/1904.06883.pdf

• RepPoints|RepPoints: 
  Point Set Representation for Object Detection
  https://arxiv.org/pdf/1904.11490.pdf

• FSAF
  Feature Selective Anchor-Free Module for Single-Shot Object Detection
  https://arxiv.org/pdf/1903.00621.pdf

• DenseBox|DenseBox: 
  Unifying Landmark Localization with End to End Object Detection
  https://arxiv.org/pdf/1509.04874.pdf

• AF-RPN
  An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches
  https://arxiv.org/ftp/arxiv/papers/1804/1804.09003.pdf

代码资源

相关的源码实现如下，大家去github上git clone一波，多数都是基于pytorch框架的，说明pytorch最近势头很猛！

• CornerNet| (official, Pytorch)
  https://github.com/princeton-vl/CornerNet

• CornerNet-Lite| (official, Pytorch)
  https://github.com/princeton-vl/CornerNet-Lite

• FCOS| (official, Pytorch)
  https://github.com/tianzhi0549/FCOS

• CenterNet^1| (official, Pytorch)
  https://github.com/xingyizhou/CenterNet

• CenterNet^2| (official, pytorch)
  https://github.com/Duankaiwen/CenterNet

• ExtremeNet| (official, Pytorch)
  https://github.com/xingyizhou/ExtremeNet

往期精选

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