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Towards Fully Decoupled End-to-End Person Search

arXiv 2309.04967 ↗First posted 10 Sep 2023cs.CVcited by 0

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Abstract (arXiv)

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for persons earch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 42 of its 55 references that are in the map. First posted on arXiv 10 Sep 2023.

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42 of 55 references.

References · 42

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30 more
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  30. 2022OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Searchcited by 12

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