Meta Clothing Status Calibration for Long-Term Person Re-Identification.

Recent studies have seen significant advancements in the field of long-term person re-identification (LT-reID) through the use of clothing-irrelevant or insensitive features. This work takes the field a step further by addressing a previously unexplored issue, the Clothing Status Distribution Shift (CSDS). CSDS refers to the differing ratios of samples with clothing changes to those without clothing changes between the training and test sets, leading to a decline in LT-reID performance. We establish a connection between the performance of LT-reID and CSDS, and argue that addressing CSDS can improve LT-reID performance. To that end, we propose a novel framework called Meta Clothing Status Calibration (MCSC), which uses meta-learning to optimize the LT-reID model. Specifically, MCSC simulates CSDS between meta-train and meta-test with meta-optimization objectives, optimizing the LT-reID model and making it robust to CSDS. This framework is designed to prevent overfitting and improve the generalization ability of the LT-reID model in the presence of CSDS. Comprehensive evaluations on seven datasets demonstrate that the proposed MCSC framework effectively handles CSDS and improves current state-of-the-art LT-reID methods on several LT-reID benchmarks.