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Learning domain invariant and specific representation for cross-domain person re-identification

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Abstract

Person re-identification (re-ID) aims to match person images under different cameras with disjoint views. Although supervised re-ID has achieved great progress, unsupervised cross-domain re-ID remains a challenging work due to domain bias. In this work, we divide cross-domain re-ID task into two phases: domain-invariant features learning and domain-specific features learning. Our contributions are twofold. (i) To achieve domain-invariant features learning, a novel model called Pedestrian General Similarity (PGS) is proposed, which can eliminate two main factors that cause domain bias: image style and background. Compared with the existing re-ID models, PGS has better generalization ability. (ii) A novel pseudo label assignment method named Mutual Nearest Neighbors Pseudo Labeling (MNNPL) is proposed, which calculates pseudo labels based on the similarity between samples in the target domain, and the resulting pseudo labels are used to guide domain-specific feature learning. Extensive experiments are conducted on several large scale datasets, the results show that our method outperforms most published unsupervised cross-domain methods by a large margin.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62072345, 41671382), LIESMARS Special Research Funding. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

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  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

    Yanwen Chong, Chengwei Peng, Chen Zhang, Yujie Wang, Wenqiang Feng & Shaoming Pan

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  1. Yanwen Chong
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Correspondence to Shaoming Pan.

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Chong, Y., Peng, C., Zhang, C. et al. Learning domain invariant and specific representation for cross-domain person re-identification. Appl Intell 51, 5219–5232 (2021). https://doi.org/10.1007/s10489-020-02107-2

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