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Disentangled Representations for Short-Term and Long-Term Person Re-Identification.
- Source :
-
IEEE Transactions on Pattern Analysis & Machine Intelligence . Dec2022, Vol. 44 Issue 12, p8975-8991. 17p. - Publication Year :
- 2022
-
Abstract
- We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons’ appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GENERATIVE adversarial networks
*IMAGE color analysis
*PERFORMANCE standards
Subjects
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 44
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 160650732
- Full Text :
- https://doi.org/10.1109/TPAMI.2021.3122444