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BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification

CVPR 2021 · Video › Video segmentation and tracking · approach: Spatial-temporal video person re-identification · 115 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Cross-Scale Paths to the two-branch network (ablation)BiCnet · MARSmAP: 84.3 -> 85.0Helped
Add diverse attention modules without diversity regularization (ablation)TB+CSP · MARSmAP: 85.0 -> 85.2No clear effect
Add a diversity loss to guide complementary attention maps (ablation)TB+CSP+AO · MARSmAP: 85.2 -> 85.6Helped
Use adaptive kernel selection instead of fixed fusion (ablation)BiCnet · MARSmAP: 85.5 -> 86.0Helped
Add a third, 64×32-resolution branch (ablation)Two-branch network · MARSmAP: 84.8 -> 79.1Hurt
Increase the small-to-big frame ratio from α=1 to α=4 (ablation)Two-branch network · MARSmAP: 84.8 -> 83.8Hurt
Add Cross-Scale Paths (ablation)Two-branch network · MARSmAP: 0.7%Helped
Add an adaptive TKS block after stage 2 (ablation)BiCnet · MARSmAP: 0.4%Helped
Use two temporal kernels of different sizes instead of one (ablation)TKS · MARSmAPHelped
Increase the temporal kernel count from two to three (ablation)TKS · MARSmAP: 86.0 -> 85.8No clear effect
Place TKS after stage 1 rather than stage 2 (ablation)BiCnet · MARSmAP: 86.0 -> 85.3Hurt
Place TKS after stage 4 rather than stage 2 (ablation)BiCnet · MARSmAP: 86.0 -> 85.4Hurt

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