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Attribute-aware style adaptation for person re-identification

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Abstract

Person re-identification (re-ID) aims to address a unique challenge in cross-camera pedestrian retrieval, especially in the case of incomplete attribute annotation. In recent years, a robust algorithm based on a generative model has been proposed that can achieve rapid convergence by extending the training data. However, these pipelines are developed separately from re-ID learning and ignore the fine-grained extension to adapt the camera style. To solve this problem, a joint learning framework is proposed in this work to implement end-to-end optimization and ultimately achieve high-quality images and impressive performance for person re-ID. In this work, an attribute-aware style adaptation based on CamStyle, called AA-CamStyle, is designed to combine fine-grained style adaptation and discriminative person re-ID. The AA-CamStyle model integrates the critical attributes into the generative learning to smooth the differences in camera style while maintaining the fine-grained information through joint representation learning of multiple styles, including attribute-aware and camera-aware. Attribute-aware (AA) strategy is applied to recommend the transmission of appropriate attributes of each pedestrian, resulting in AA-CamStyle’s tremendous quality of translated images compared to existing models. We empirically demonstrate the effectiveness of the proposed approach on person re-ID tasks.

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Acknowledgements

The work is partially supported by the National Natural Science Foundation of China (Nos. U1836216, 61772322, 62076153), the major fundamental research project of Shandong, China (No. ZR2019ZD03), the Taishan Scholar Project of Shandong, China (No. ts20190924), and CCF-Baidu Open Fund (Grant: CCF-BAIDU OF2022008).

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  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Xiaofeng Qu, Li Liu, Lei Zhu & Huaxiang Zhang

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  1. Xiaofeng Qu
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Our source codes and testing datasets can be obtained at https://github.com/XiaofengQu/AA-CamStyle.

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Qu, X., Liu, L., Zhu, L. et al. Attribute-aware style adaptation for person re-identification. Multimedia Systems 29, 469–485 (2023). https://doi.org/10.1007/s00530-022-01024-3

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