Papers › Benchmarks for Corruption Invariant Person Re-identification

Benchmarks for Corruption Invariant Person Re-identification

1 Nov 2021arXiv:2111.00880archive 2025-07-28

Minghui Chen, Zhiqiang Wang, Feng Zheng

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.

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Tasks

Cross-Modal Person Re-IdentificationGeneralizable Person Re-identificationPerson Re-Identification

Datasets

Introduced by this paper, per the archive.

CUHK03-CMSMT17-CMarket-1501-CRegDB-CSYSU-MM01-C

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Person Re-Identification CUHK03-C CIL (ResNet-50) Rank-1 22.96 #8 of 8 Archive leaderboard report
Person Re-Identification CUHK03-C CIL (ResNet-50) mAP 16.33 #8 of 8 Archive leaderboard report
Person Re-Identification CUHK03-C CIL (ResNet-50) mINP 22.96 #8 of 8 Archive leaderboard report
Person Re-Identification MSMT17-C CIL (ResNet-50) Rank-1 39.79 #5 of 5 Archive leaderboard report
Person Re-Identification MSMT17-C CIL (ResNet-50) mAP 15.33 #5 of 5 Archive leaderboard report
Person Re-Identification MSMT17-C CIL (ResNet-50) mINP 0.32 #5 of 5 Archive leaderboard report
Person Re-Identification Market-1501-C CIL (ResNet-50) Rank-1 55.57 #21 of 22 Archive leaderboard report
Person Re-Identification Market-1501-C CIL (ResNet-50) mAP 28.03 #21 of 22 Archive leaderboard report
Person Re-Identification Market-1501-C CIL (ResNet-50) mINP 1.76 #21 of 22 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) Rank-1 (All Search) 36.95 #2 of 2 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) Rank-1 (Indoor Search) 40.73 #2 of 2 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) mAP (All Search) 35.92 #2 of 2 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) mAP (Indoor Search) 48.65 #2 of 2 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) mINP (All Search) 22.48 #2 of 2 Archive leaderboard report
Person Re-Identification SYSU-MM01-C CIL (ResNet-50) mINP (Indoor Search) 43.11 #2 of 2 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AugMixRandom Erasing

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