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Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-Identification

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification · 510 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace average pooling with dual attentive sequence matching (ablation)DenseNet-121 person ReID · Market-1501R1: 74.20 -> 79.66Helped
replace average pooling with dual attentive sequence matching (ablation)DenseNet-121 person ReID · DukeMTMC-reIDR1: 64.05 -> 68.40Helped
replace average pooling with dual attentive sequence matching (ablation)DenseNet-121 person ReID · MARSR1: 65.45 -> 66.36Helped
use intra-sequence attention only instead of dual attention (ablation)DuATM · Market-1501R1: 79.66 -> 78.78Hurt
use inter-sequence attention only instead of dual attention (ablation)DuATM · Market-1501R1: 79.66 -> 72.36Hurt
add decorrelation loss (ablation)DuATM with triplet loss · Market-1501R1: 79.66 -> 81.83Helped
add decorrelation loss (ablation)DuATM with triplet loss · DukeMTMC-reIDR1: 68.40 -> 69.17Helped
add decorrelation loss (ablation)DuATM with triplet loss · MARSR1: 66.36 -> 66.52Helped
add cross-entropy identity loss (ablation)DuATM with triplet loss · Market-1501R1: 79.66 -> 87.50Helped
add cross-entropy identity loss (ablation)DuATM with triplet loss · DukeMTMC-reIDR1: 68.40 -> 79.40Helped
add cross-entropy identity loss (ablation)DuATM with triplet loss · MARSR1: 66.36 -> 73.74Helped
increase decorrelation-loss weight beyond a moderate value (ablation)DuATM · Market-1501, DukeMTMC-reID, and MARSR1Hurt

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