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Rethinking Temporal Fusion for Video-Based Person Re-Identification on Semantic and Time Aspect

AAAI 2020 · Video › Video segmentation and tracking · approach: Spatial-temporal video person re-identification · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use multi-stage fusion instead of single-stage late fusion (ablation)Video-based ReID · MARSMAP: 75.4 -> 75.6Helped
Add semantic attention to multi-stage fusion (ablation)MS Fusion · MARSMAP: 75.6 -> 77.7Helped
Use intra-frame attention instead of average pooling (ablation)Late-fusion temporal aggregation · MARSMAP: 75.4 -> 76.6Helped
Use Euclidean inter-frame attention instead of average pooling (ablation)Late-fusion temporal aggregation · MARSMAP: 75.4 -> 75.7Helped
Use Relation Network inter-frame attention instead of average pooling (ablation)Late-fusion temporal aggregation · MARSMAP: 75.4 -> 76.8Helped
Combine intra-frame and Euclidean inter-frame attention (ablation)Late-fusion temporal aggregation · MARSMAP: 76.6 -> 79.1Helped
Use Relation Network rather than Euclidean distance for inter-frame attention (ablation)Intra/inter-frame attention · MARSMAP: 79.1 -> 82.4Helped
Add multi-stage fusion to intra/inter-frame Relation Network attention (ablation)Intra/Inter-frame (RN) · MARSMAP: 82.4 -> 85.2Helped
Use intra/inter-frame Relation Network attention and multi-stage fusion instead of VRSTCVideo-based ReID · MARSMAP: 82.3 -> 85.2Helped
Use intra/inter-frame Relation Network attention and multi-stage fusion instead of VRSTCVideo-based ReID · MARStop1: 88.5 -> 87.1Hurt
Use the proposed multi-stage temporal fusion method instead of Attribute DrivenVideo-based ReID · PRID2011top1: 93.9 -> 95.8Helped
Use the proposed multi-stage temporal fusion method instead of Attribute DrivenVideo-based ReID · i-LIDS-VIDtop1: 86.3 -> 87.7Helped

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