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Bipartite Graph Network With Adaptive Message Passing for Unbiased Scene Graph Generation

CVPR 2021 · Graph learning › Knowledge and temporal graphs · approach: Scene graph generation · 253 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add bipartite graph message passing (ablation)Plain scene graph baseline · Visual Genome SGGenmR@100: 9.7 -> 10.5Helped
Add confidence-aware message propagation (ablation)BGNN · Visual Genome SGGenmR@100: 10.5 -> 11.7Helped
Add multi-stage refinement (ablation)BGNN · Visual Genome SGGenmR@100: 11.7 -> 12.6Helped
Use bi-level data sampling instead of no resampling (ablation)BGNN · Visual Genome SGGenmR@100: 9.7 -> 12.6Helped
Replace bi-level sampling with repeat factor sampling (ablation)BGNN · Visual Genome SGGenmR@100: 12.6 -> 10.7Hurt
Increase iterations from 1 to 3 in one stage (ablation)BGNN · Visual Genome SGGenmR@100: 10.0 -> 10.8Helped
Increase stages from 1 to 3 while using 3 iterations (ablation)BGNN · Visual Genome SGGenmR@100: 10.8 -> 12.6Helped
Increase stages from 3 to 4 while using 3 iterations (ablation)BGNN · Visual Genome SGGenmR@100: 12.6 -> 12.5Hurt
Use BGNN rather than UnbiasedResNeXt-101-FPN scene graph model · Visual Genome PredClsmR@50: 25.4 -> 30.4Helped
Use BGNN rather than RelDNOpen Images V4 scene graph model · Open Images V4mR@50: 70.40 -> 72.11Helped
Use BGNN rather than GPS-Net with resamplingOpen Images V6 scene graph model · Open Images V6mR@50: 38.93 -> 40.45Helped

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