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ATSS: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

May 2020

tl;dr: The gap between anchor-based and anchor-free methods lie in the sampling strategy.

Overall impression

The paper founds out that the main difference between anchor-based methods (such as RetinaNet) and anchor-free methods (FCOS) mainly lies in the definition of positive examples and negative examples.

This paper draws much inspiration with FCOS and shall be read together. For anchor based networks With ATSS, one bbox

Key ideas

  • The paper first uses the same tricks for both RetinaNet and FCOS to make sure these training tricks are accounted for during comparison
    • GroupNorm in heads
    • GIoU loss
    • In GT bbox: positive samples should be inside GT bbox
    • Add a trainable scaler for each FPN level
    • Introducing centerness branch
  • Selection of positive samples and negative samples
    • RetinaNet uses IoU to select positive anchor bbox
    • FCOS uses spatial constraint (anchor point should be inside GT) and scale constraints (uses predefined ranges to assign GT to different levels of FPN)
    • ATSS:
      • from each level L select k anchors (kL in total), whose centers are closest to GT bbox
      • Compute IoU and mean and std (for anchor-free method, the anchor point is converted to anchor box with 8S scale)
      • Select bbox with IoU > mean + std and center of bbox in GT (usually 20% * kL
  • Regressing from a point or a preset anchor box yields the same results.
  • For IoU based sampling, more anchor box is helpful. For ATSS, one bbox is enough.

Technical details

  • Summary of technical details

Notes

  • Questions and notes on how to improve/revise the current work