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Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification

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Abstract

Person re-identification (Re-ID) in real-world scenarios suffers from various degradations, e.g., low resolution, weak lighting, and bad weather. These degradations hinders identity feature learning and significantly degrades Re-ID performance. To address these issues, in this paper, we propose a degradation invariance learning framework for robust person Re-ID. Concretely, we first design a content-degradation feature disentanglement strategy to capture and isolate task-irrelevant features contained in the degraded image. Then, to avoid the catastrophic forgetting problem, we introduce a memory replay algorithm to further consolidate invariance knowledge learned from the previous pre-training to improve subsequent identity feature learning. In this way, our framework is able to continuously maintain degradation-invariant priors from one or more datasets to improve the robustness of identity features, achieving state-of-the-art Re-ID performance on several challenging real-world benchmarks with a unified model. Furthermore, the proposed framework can be extended to low-level image processing, e.g., low-light image enhancement, demonstrating the potential of our method as a general framework for the various vision tasks. Code and trained models will be available at: https://github.com/hyk1996/Degradation-Invariant-Re-ID-pytorch.

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  1. https://sites.google.com/site/vonikakis/datasets

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Acknowledgements

This work is supported by the National Key R &D Program of China under Grant 2020AAA0105702, the National Natural Science Foundation of China (NSFC) under Grants U19B2038 and 61901433, the University Synergy Innovation Program of Anhui Province under Grants GXXT-2019-025, the Fundamental Research Funds for the Central Universities under Grant WK2100000024, and the USTC Research Funds of the Double First-Class Initiative under Grant YD2100002003.

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Correspondence to Xueyang Fu.

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Huang, Y., Fu, X., Li, L. et al. Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification. Int J Comput Vis 130, 2770–2796 (2022). https://doi.org/10.1007/s11263-022-01666-w

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