Methods › Computer Vision › Person Search Models › AlignPS
Feature-Aligned Person Search Network
AlignPS
Introduced by Yichao Yan et al. in Efficient Person Search: An Anchor-Free Approach
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
AlignPS, or Feature-Aligned Person Search Network, is an anchor-free framework for efficient person search. The model employs the typical architecture of an anchor-free detection model (i.e., FCOS). An aligned feature aggregation (AFA) module is designed to make the model focus more on the re-id subtask. Specifically, AFA reshapes some building blocks of FPN to overcome the issues of region and scale misalignment in re-id feature learning. A deformable convolution is exploited to make the re-id embeddings adaptively aligned with the foreground regions. A feature fusion scheme is designed to better aggregate features from different FPN levels, which makes the re-id features more robust to scale variations. The training procedures of re-id and detection are also optimized to place more emphasis on generating robust re-id embeddings.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Head and Body: Unified Detector and Graph Network for Person Search in Media 27 Nov 2021 · 0 repositories · arXiv:2111.13888
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Efficient Person Search: An Anchor-Free Approach 1 Sep 2021 · 4 repositories · arXiv:2109.00211
Tasks archive 2025-07-28
1 task the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Person Search | 2 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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