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Transformer for Object Re-identification: A Survey

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Abstract

Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period, this field has been predominantly driven by deep learning technology based on convolutional neural networks. In recent years, the emergence of Vision Transformers has spurred a growing number of studies delving deeper into Transformer-based Re-ID, continuously breaking performance records and witnessing significant progress in the Re-ID field. Offering a powerful, flexible, and unified solution, Transformers cater to a wide array of Re-ID tasks with unparalleled efficacy. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single/cross modal tasks. For the under-explored animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task and facilitate future research. Finally, we discuss some important yet under-investigated open issues in the large foundation model era, we believe it will serve as a new handbook for researchers in this field. A periodically updated website will be available at https://github.com/mangye16/ReID-Survey.

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Data Availability

The authors declare that publicly available datasets are utilized for evaluating object Re-ID methods. The data supporting the experiments conducted in this study can be found in the paper. All publicly available datasets about person-related data used in this study are obtained and utilized following appropriate permissions and ethical guidelines.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (62176188, 62361166629, 62225113).

Funding

National Natural Science Foundation of China (62176188, 62361166629, 62225113).

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Authors and Affiliations

  1. The National Engineering Research Center for Multimedia Software, School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China

    Mang Ye, Shuoyi Chen, Chenyue Li & Bo Du

  2. The School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  3. The Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, USA

    David Crandall

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  1. Mang Ye
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  2. Shuoyi Chen
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Communicated by Bumsub Ham.

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Ye, M., Chen, S., Li, C. et al. Transformer for Object Re-identification: A Survey. Int J Comput Vis 133, 2410–2440 (2025). https://doi.org/10.1007/s11263-024-02284-4

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