Summary of Mitigating Object Hallucination in Large Vision-language Models Via Classifier-free Guidance, by Linxi Zhao and Yihe Deng and Weitong Zhang and Quanquan Gu
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance
by Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 introduces MARINE, a framework that mitigates object hallucination in Large Vision-Language Models (LVLMs) without requiring expensive training or API access. MARINE enriches visual context and incorporates classifier-free guidance to improve LVLM precision. This approach outperforms existing fine-tuning-based methods, reducing hallucinations and improving generation detailness. The paper demonstrates the effectiveness of MARINE through comprehensive evaluations across 6 popular LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us make AI models better at recognizing what’s in pictures without making up things that aren’t there. They created a special tool called MARINE to fix this problem, and it works! They tested it with many different AI models and found that it actually makes the models do a better job of describing what they see. This is important because we want our AI models to be accurate and reliable. |
Keywords
* Artificial intelligence * Fine tuning * Hallucination * Precision
