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High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification

CVPR 2020 · Graph learning › Knowledge and temporal graphs · approach: Graph reasoning for visual recognition · 497 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HOReID with adaptive relation learning and graph-based topology alignmentResNet50 person ReID · Occluded-DukeRank-1: 51.4 -> 55.1Helped
Use HOReID with adaptive relation learning and graph-based topology alignmentResNet50 person ReID · Occluded-DukemAP: 37.3 -> 43.8Helped
Use HOReIDResNet50 person ReID · Occluded-ReIDRank-1: 78.3 -> 80.3Helped
Use HOReIDResNet50 person ReID · Occluded-ReIDmAP: 77.9 -> 70.2Hurt
Use HOReIDResNet50 person ReID · Partial-REIDRank-1: 82.7 -> 85.3Helped
Use HOReIDResNet50 person ReID · Partial-iLIDSRank-1: 69.1 -> 72.6Helped
Use HOReIDResNet50 person ReID · Market-1501Rank-1: 95.4 -> 94.2Hurt
Use HOReIDResNet50 person ReID · DukeMTMC-reIDRank-1: 88.6 -> 86.9Hurt
Add the semantic module to the global-feature-only IDE baseline (ablation)HOReID · Occluded-DukeRank-1: 49.9 -> 52.4Helped
Add the high-order relation module (ablation)HOReID with semantic module · Occluded-DukeRank-1: 52.4 -> 53.9Helped
Add the high-order topology module (ablation)HOReID with semantic and relation modules · Occluded-DukeRank-1: 53.9 -> 55.1Helped
Add key-point confidence normalization (ablation)HOReID · Occluded-DukeRank-1: 52.5 -> 55.1Helped

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