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Benchmarks for Corruption Invariant Person Re-identification
[article]
2022
arXiv
pre-print
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
arXiv:2111.00880v2
fatcat:7tvgf5sgwjbitmosfgfhs7bcmi