Towards Fully Decoupled End-to-End Person Search Article Swipe
Pengcheng Zhang
,
Xiao Bai
,
Jin Zheng
,
Ning Xin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.04967
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.04967
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.04967
- https://arxiv.org/pdf/2309.04967
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386646786
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386646786Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2309.04967Digital Object Identifier
- Title
-
Towards Fully Decoupled End-to-End Person SearchWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-10Full publication date if available
- Authors
-
Pengcheng Zhang, Xiao Bai, Jin Zheng, Ning XinList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.04967Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.04967Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.04967Direct OA link when available
- Concepts
-
Task (project management), Computer science, End-to-end principle, Artificial intelligence, Construct (python library), Machine learning, Engineering, Computer network, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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