  The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP)
method for occluded person re-identification (ReID). Unlike mainstream
approaches using global features for simultaneous multi-task learning of ReID
and human parsing, or relying on semantic information for attention guidance,
DROP argues that the inferior performance of the former is due to distinct
granularity requirements for ReID and human parsing features. ReID focuses on
instance part-level differences between pedestrian parts, while human parsing
centers on semantic spatial context, reflecting the internal structure of the
human body. To address this, DROP decouples features for ReID and human
parsing, proposing detail-preserving upsampling to combine varying resolution
feature maps. Parsing-specific features for human parsing are decoupled, and
human position information is exclusively added to the human parsing branch. In
the ReID branch, a part-aware compactness loss is introduced to enhance
instance-level part differences. Experimental results highlight the efficacy of
DROP, especially achieving a Rank-1 accuracy of 76.8% on Occluded-Duke,
surpassing two mainstream methods. The codebase is accessible at
https://github.com/shuguang-52/DROP.
