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Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification

CVPR 2020 · Video › Video segmentation and tracking · approach: Self-supervised video representation learning · 131 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace global spatial-temporal average pooling with MG-RAFAResNet-50 video re-identification · MARSmAP: 82.1 -> 85.9Helped
replace global spatial-temporal average pooling with MG-RAFAResNet-50 video re-identification · iLIDS-VIDRank-1: 86.5 -> 88.6Helped
replace global spatial-temporal average pooling with MG-RAFAResNet-50 video re-identification · PRID2011Rank-1: 92.5 -> 95.9Helped
add global reference-node relations to appearance-only attention (ablation)MG-AFA (N=4) · MARSRank-1: 86.6 -> 88.8Helped
add global reference-node relations to appearance-only attention (ablation)MG-AFA (N=4) · iLIDS-VIDRank-1: 86.7 -> 88.6Helped
add global reference-node relations to appearance-only attention (ablation)MG-AFA (N=4) · PRID2011Rank-1: 92.6 -> 95.9Helped
add multiple granularity levels instead of using a single granularity (ablation)SG-RAFA (S=1) · MARSRank-1: 87.8 -> 88.8Helped
add multiple granularity levels instead of using a single granularity (ablation)SG-RAFA (S=1) · iLIDS-VIDRank-1: 87.1 -> 88.6Helped
add multiple granularity levels instead of using a single granularity (ablation)SG-RAFA (S=1) · PRID2011Rank-1: 93.6 -> 95.9Helped
increase the granularity count from two to four (ablation)MG-RAFA · MARSmAP: 85.5 -> 85.9Helped
use channel-split attention at one shared granularity rather than different granularities (ablation)SG-RAFA (S=4) · MARSmAP: 84.9 -> 85.9Hurt
average-pool reference nodes across time to form a representative set (ablation)SG-RAFA · MARSmAP: 84.3 -> 85.1Helped

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