Summary of Dive: Towards Descriptive and Diverse Visual Commonsense Generation, by Jun-hyung Park et al.
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation
by Jun-Hyung Park, Hyuntae Park, Youjin Kang, Eojin Jeon, SangKeun Lee
First submitted to arxiv on: 15 Aug 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 paper proposes a novel framework, DIVE, for generating descriptive and diverse visual commonsense inferences. The framework combines two methods: generic inference filtering and contrastive retrieval learning. This approach addresses limitations in existing visual commonsense resources and training objectives. Experimental results show that DIVE outperforms state-of-the-art models in terms of descriptiveness, diversity, and quality of generated inferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DIVE is a new way to make computers better understand pictures. It can generate descriptions and ideas based on what’s in the picture. This helps it be more creative and clever. The DIVE system uses two special techniques to get even better at this. It looks at how humans think and learn, and that helps it do a better job. In tests, DIVE did really well and was able to come up with new and interesting ideas about pictures. |
Keywords
» Artificial intelligence » Inference
