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Research map / Detection and segmentation / Open-set and few-shot detection

Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection

CVPR 2017 · Detection and segmentation › Open-set and few-shot detection · approach: Visual relationship and scene graph detection · 248 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use VRL instead of Lu et al.relationship phrase detection on VRD · VRDPhr. R@100: 17.03 -> 22.60Helped
Use VRL instead of Lu et al.relationship detection on Visual Genome · Visual GenomeRel. R@100: 7.96 -> 13.34Helped
Use VRL instead of Joint CNN+Trained RPNattribute detection · Visual GenomeAttribute Recall@100: 9.77 -> 26.43Helped
Use variation-structured action sets instead of the full action space (ablation)VRL · Visual GenomeRel. R@100: 6.23 -> 13.34Helped
Add history phrase embeddings to the state (ablation)VRL · Visual GenomeRel. R@100: 9.05 -> 13.34Helped
Use semantic phrase embeddings instead of historical action vectors (ablation)VRL · Visual GenomeRel. R@100: 11.98 -> 13.34Helped
Use category-based object mining instead of directional actions (ablation)VRL · Visual GenomeRel. R@100: 10.66 -> 13.34Helped
Include ambiguous candidate object categories during object mining (ablation)VRL · Visual GenomeRel. R@100: 12.01 -> 13.34Helped
Replace fully connected layers with LSTM layers (ablation)VRL · Visual GenomeRel. R@100: 13.34 -> 13.86No clear effect
Use sequential RL mining instead of random-walk traversal (ablation)relationship and attribute detection · Visual GenomeRel. R@100: 3.67 -> 6.23Helped
Use VRL rather than Lu et al. for zero-shot relationship detectionVRD · VRD zero-shotRel. R@100: 3.28 -> 8.52Helped

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