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Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification

CVPR 2022 · Label-efficient and robust learning › Recognition and long-tail · approach: Supervised person re-identification · 213 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add unidirectional metrics (ablation)Baseline on SYSU-MM01 · SYSU-MM01 all-searchRank-1: 49.45 -> 55.97Helped
Add unidirectional metrics (ablation)Baseline on SYSU-MM01 · SYSU-MM01 all-searchmAP: 47.21 -> 53.42Helped
Add memory-based augmentation (ablation)MAUM without memory augmentation on SYSU-MM01 · SYSU-MM01 all-searchRank-1: 55.97 -> 61.59Helped
Add memory-based augmentation (ablation)MAUM without memory augmentation on SYSU-MM01 · SYSU-MM01 all-searchmAP: 53.42 -> 59.96Helped
Use part features instead of global featuresMAUM on SYSU-MM01 · SYSU-MM01 all-searchRank-1: 61.59 -> 71.68Helped
Use part features instead of global featuresMAUM on RegDB · RegDB visible to infraredRank-1: 83.39 -> 87.87Helped
Use memory-based augmentation with modality-specific memory banksMAUM on SYSU-MM01 with 5 IR images per identity · SYSU-MM01 modality-imbalance scenario, N=5mAP: 33.7 (-36.9%) -> 43.2 (-26.8%)Helped
Use memory-based augmentation with modality-specific memory banksMAUM on SYSU-MM01 with 395 RGB and 50 IR identities · SYSU-MM01 modality-fragmentary scenariomAP: 24.2 (-54.7%) -> 36.1 (-38.8%)Helped
Vary memory-bank size (ablation)MAUM on SYSU-MM01 · SYSU-MM01 all-searchRank-1Mixed
Vary proxy sampling interval (ablation)MAUM on SYSU-MM01 · SYSU-MM01 all-searchRank-1Mixed

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