Summary of Sgtr+: End-to-end Scene Graph Generation with Transformer, by Rongjie Li et al.
SGTR+: End-to-end Scene Graph Generation with Transformer
by Rongjie Li, Songyang Zhang, Xuming He
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenging task of Scene Graph Generation (SGG), where a compositional approach is crucial. The proposed method, SGT-R, formulates SGG as a bipartite graph construction problem and develops an end-to-end transformer-based framework to generate entity and predicate proposals. A graph assembling module infers directed edges and connectivity, enabling efficient scene graph generation. The paper introduces an enhanced entity-aware design for optimization stability and efficacy. Experimental results demonstrate state-of-the-art or comparable performance on three benchmarks, outperforming most existing approaches while maintaining high efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better understand images by creating a “scene graph” that shows what’s in the picture. The scientists developed a new way to do this called SGT-R. They created a special computer program that can look at an image and identify objects, actions, and relationships between them. This allows the computer to generate a detailed scene graph quickly and accurately. The team tested their method on three challenging datasets and found it outperformed most other approaches while being efficient in its processing. |
Keywords
» Artificial intelligence » Optimization » Transformer
