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MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification

AAAI 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add MRCN to the baseline (ablation)VI-ReID model · RegDBRank-1: 78.2 -> 91.4Helped
Add MRCN to the baseline (ablation)VI-ReID model · SYSU-MM01Rank-1: 60.7 -> 68.9Helped
Add the CQC loss (ablation)MRCN · RegDBRank-1: 85.5 -> 91.4Helped
Add the CQC loss (ablation)MRCN · SYSU-MM01Rank-1: 64.6 -> 68.9Helped
Add MRM to MCM (ablation)MRCN · RegDBRank-1: 89.7 -> 91.4Helped
Add MRM to MCM (ablation)MRCN · SYSU-MM01Rank-1: 64.4 -> 68.9Helped
Add MCM to MRM (ablation)MRCN · RegDBRank-1: 90.3 -> 91.4Helped
Add MCM to MRM (ablation)MRCN · SYSU-MM01Rank-1: 66.8 -> 68.9Helped
Plug MRCN after stage-0 instead of stage-1 (ablation)MRCN with ResNet-50 · RegDBRank-1: 91.4 -> 87.8Hurt
Plug MRCN after stage-2 instead of stage-1 (ablation)MRCN with ResNet-50 · RegDBRank-1: 91.4 -> 88.1Hurt
Plug MRCN after stage-3 instead of stage-1 (ablation)MRCN with ResNet-50 · RegDBRank-1: 91.4 -> 73.5Hurt
Plug MRCN after stage-4 instead of stage-1 (ablation)MRCN with ResNet-50 · RegDBRank-1: 91.4 -> 72.0Hurt

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