AlignPS
Feature-Aligned Person Search Network
**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](https://paperswithcode.com/method/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](https://paperswithcode.com/method/fpn) to overcome the issues of region and scale misalignment in re-id feature learning. A [deformable convolution](https://paperswithcode.com/method/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.
Efficient Person Search: An Anchor-Free Approach
https://paperswithcode.com/paper/efficient-person-search-an-anchor-free
Efficient Person Search: An Anchor-Free Approach