pss.md

PSS: Object Detection Made Simpler by Eliminating Heuristic NMS

January 2021

tl;dr: Add positive sample selector (PSS) branch to FCOS to achieve end-to-end detection.

Overall impression

PSS keeps FCOS's structure and training scheme as much as possible, and introduced one positive sample selector branch.

PSS uses a simple binary classification head to enable selection of one positive sample for each instance, while DeFCN uses 3D max filtering to aggregate multi-scale features to suppress duplicated detection.

Key ideas

• PSS branch: one additional head attached to regression head.
• PSS loss. Cross entropy loss between GT and pred of $\hat{P}= \sigma(\text{cls}) \sigma(\text{centerness}) \sigma(\text{pss})$.
  • Still train FOCS with one-to-many label assignment. This helps convergence as DeFCN still uses it as auxiliary loss.
• Stop gradient operation to reconcile the conflict between PSS classification loss and the original FCOS loss.
  • This is in theory equivalent to training the original FCOS until convergence and freezing FCOS, and then training the PSS head only until convergence (thus PSS is a learnable NMS). In practice, two-step training leads to slightly worse performance.

Technical details

• In FCOS, classication head and regression head are sibling heads. This is different from CenterNet
• Attaching the PSS head to classification head leads to worse performance. This is similar to that centerness head needs to be attached to regression head too, as observed in FCOS.

Notes

• Review by 1st author on Zhihu知乎
• Code on github