Person Transfer GAN to Bridge Domain Gap for Person Re-identification
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Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT171 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
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Generative adversarial networks have been used to generate style-transferred images for bridging the domain gap between different datasets in person re-identification.
“Recently, generative adversarial networks (GAN) have been used to generate style-transferred images for bridging the domain gap between different datasets [111].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [111]
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- Authors
- Wei, L., Zhang, S., Gao, W., & Tian, Q.
- Affiliations
- Peking University (CN)
- The University of Texas at San Antonio (US)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 79-88
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963047834
- Cited by
- 2,118
- References
- 45
- Citations by year
- 2026: 135 · 2025: 275 · 2024: 295 · 2023: 327 · 2022: 343 · 2021: 341 · 2020: 214 · 2019: 170 · 2018: 18
- FWCI
- 55.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- person re-identification, domain gap, generative adversarial networks, multi-camera systems, illumination variation, domain generalization
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