Research map / Graph learning / Knowledge and temporal graphs
Generating Triples With Adversarial Networks for Scene Graph Construction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SG-GAN instead of the prior state-of-the-art method | Scene graph generation · Visual Genome, top 150 objects and 50 relations split | recall@50: 3.44 -> 6.84 | Helped |
| Use SG-GAN instead of the prior state-of-the-art method | Scene graph generation · Visual Genome, top 150 objects and 50 relations split | recall@100: 4.24 -> 8.95 | Helped |
| Expand the vocabulary and include attribute triples | SG-GAN · Visual Genome, custom split with 400 objects, 150 relations, and 150 attributes | recall@50 | Hurt |
| Expand the vocabulary and include attribute triples | SG-GAN · Visual Genome, custom split with 400 objects, 150 relations, and 150 attributes | recall@100 | Hurt |
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- DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation (CVPR 2024 · similarity 0.88)
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