Research map / Graph learning / Knowledge and temporal graphs
Scene Graph Generation by Iterative Message Passing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add iterative contextual message passing to make joint object and relationship predictions | Visual Genome predicate classification · Visual Genome Pred Cls | R@100: 35.04 -> 53.08 | Helped |
| use learned weighted-sum message pooling instead of average pooling (ablation) | Scene Graph Generation by Iterative Message Passing · Visual Genome Pred Cls | R@100: 39.63 -> 53.08 | Helped |
| use learned weighted-sum message pooling instead of max pooling (ablation) | Scene Graph Generation by Iterative Message Passing · Visual Genome Pred Cls | R@100: 41.99 -> 53.08 | Helped |
| train with two message-passing iterations rather than more iterations (ablation) | Scene Graph Generation by Iterative Message Passing · Visual Genome Pred Cls | R@100 | Helped |
| use contextual iterative inference rather than isolated predictions | NYU Depth v2 support graph · NYU Depth v2 support graph | Type-aware support accuracy: 85.3 -> 89.0 | Helped |
| use contextual iterative inference rather than isolated predictions | NYU Depth v2 predicate classification · NYU Depth v2 Pred Cls | R@100: 50.3 -> 55.5 | Helped |
Closest papers by meaning
- LinkNet: Relational Embedding for Scene Graph (NeurIPS 2018 · similarity 0.93)
- Joint Modeling of Visual Objects and Relations for Scene Graph Generation (NeurIPS 2021 · similarity 0.93)
- Image Generation From Scene Graphs (CVPR 2018 · similarity 0.93)
- DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation (CVPR 2024 · similarity 0.92)
- Scene Graph-Grounded Image Generation (AAAI 2025 · similarity 0.92)
- From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models (CVPR 2024 · similarity 0.91)
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