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IS-GGT: Iterative Scene Graph Generation With Generative Transformers

CVPR 2023 · Graph learning › Knowledge and temporal graphs · approach: Scene graph generation · 28 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Generate a sparse interaction graph before predicate classificationIS-GGT · Visual Genome, PredCls, SGCls, and SGDetmR@100Mixed
Add semantic features to relation prediction (ablation)IS-GGT · Visual Genome PredClsmR@100: 28.3 -> 30.1Helped
Replace GloVe with ConceptNet Numberbatch embeddings (ablation)IS-GGT · Visual Genome PredClsmR@100: 30.1 -> 28.5No clear effect
Add visual features to relation prediction (ablation)IS-GGT · Visual Genome SGClsmR@100: 15.2 -> 17.4Helped
Add global image context to predicate prediction (ablation)IS-GGT · Visual Genome PredClsmR@100: 29.3 -> 30.1Helped
Add graph sampling (ablation)IS-GGT · Visual Genome PredClsmR@100: 27.9 -> 30.1Helped
Use an edge prior to rank sampled edges (ablation)IS-GGT · Visual Genome SGDetmR@100: 9.3 -> 11.9Helped
Use detector-provided node labels instead of predicted node labels (ablation)IS-GGT · Visual Genome SGDetmR@100: 8.9 -> 11.9Helped
Increase the maximum considered edges from 10 to 250 (ablation)IS-GGT · Visual Genome PredClsmR@100: 4.6 -> 30.1Helped
Increase the maximum considered edges from 250 to all edges (ablation)IS-GGT · Visual Genome SGClsmR@100: 17.5 -> 17.6No clear effect

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