  When deploying person re-identification (ReID) model in safety-critical
applications, it is pivotal to understanding the robustness of the model
against a diverse array of image corruptions. However, current evaluations of
person ReID only consider the performance on clean datasets and ignore images
in various corrupted scenarios. In this work, we comprehensively establish six
ReID benchmarks for learning corruption invariant representation. In the field
of ReID, we are the first to conduct an exhaustive study on corruption
invariant learning in single- and cross-modality datasets, including
Market-1501, CUHK03, MSMT17, RegDB, SYSU-MM01. After reproducing and examining
the robustness performance of 21 recent ReID methods, we have some
observations: 1) transformer-based models are more robust towards corrupted
images, compared with CNN-based models, 2) increasing the probability of random
erasing (a commonly used augmentation method) hurts model corruption
robustness, 3) cross-dataset generalization improves with corruption robustness
increases. By analyzing the above observations, we propose a strong baseline on
both single- and cross-modality ReID datasets which achieves improved
robustness against diverse corruptions. Our codes are available on
https://github.com/MinghuiChen43/CIL-ReID.
