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

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/Degradati on-Invariant-Re-D-pytorch.