Summary of Reflexive Guidance: Improving Oodd in Vision-language Models Via Self-guided Image-adaptive Concept Generation, by Jihyo Kim et al.
Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation
by Jihyo Kim, Seulbi Lee, Sangheum Hwang
First submitted to arxiv on: 19 Oct 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 trustworthiness of foundation models, specifically large vision-language models (LVLMs) like GPT-4o, which are trained on massive multi-modal data. Despite their impressive generalization capabilities and widespread adoption across various application domains, the out-of-distribution detection (OoDD) capabilities of these models remain underexplored. The authors evaluate and analyze the OoDD capabilities of various proprietary and open-source LVLMs to better understand how they represent confidence scores through generated natural language responses. They also propose a self-guided prompting approach called Reflexive Guidance (ReGuide) to enhance the OoDD capability of LVLMs by leveraging self-generated image-adaptive concept suggestions. Experimental results demonstrate that ReGuide improves the performance of current LVLMs in both image classification and OoDD tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure big language models are trustworthy and can be used safely. These models are very good at understanding images, text, and other data, but they’re not perfect. Sometimes they make mistakes or get confused when shown something new. The researchers studied how well these models do at detecting when they don’t understand something and proposed a way to improve their performance. They tested this approach on several different models and found that it worked better than the original models. |
Keywords
» Artificial intelligence » Generalization » Gpt » Image classification » Multi modal » Prompting
