Summary of Semantic Diversity-aware Prototype-based Learning For Unbiased Scene Graph Generation, by Jaehyeong Jeon et al.
Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation
by Jaehyeong Jeon, Kibum Kim, Kanghoon Yoon, Chanyoung Park
First submitted to arxiv on: 22 Jul 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 The proposed Semantic Diversity-aware Prototype-based Learning (DPL) framework is a novel approach to address the limitations in current scene graph generation (SGG) models. By understanding the semantic diversity of predicates, DPL enables unbiased predictions for SGG tasks. The framework learns regions in the semantic space covered by each predicate, distinguishing between different semantics that a single predicate can represent. Compared to existing SGG models, our proposed framework shows significant performance improvement and effectively understands predicate semantics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve scene graph generation (SGG) models. These models are not very good at understanding the many different meanings of certain words or phrases in images. The researchers created a new approach called DPL that helps the model learn about these different meanings. This makes the predictions more accurate and fair. They tested their new method and found it works really well. |
Keywords
» Artificial intelligence » Semantics
