Summary of Make Vlm Recognize Visual Hallucination on Cartoon Character Image with Pose Information, by Bumsoo Kim et al.
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information
by Bumsoo Kim, Wonseop Shin, Kyuchul Lee, Yonghoon Jung, Sanghyun Seo
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 novel semantic structural hallucination detection system leverages Vision-Language Models (VLMs) to improve the accuracy of identifying visual hallucinations in non-photorealistic rendering (NPR) domains. The approach utilizes large language models’ emerging capability for in-context learning, which enables VLMs to make more accurate decisions when provided with pose information and RGB images. Experimental results demonstrate significant improvements in hallucination detection compared to baseline methods, with the proposed PA-ICVL achieving 50%-78% accuracy with GPT-4v and 57%-80% accuracy with Gemini Pro Vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new system to detect visual hallucinations in cartoons and other non-realistic images. This problem is important because it can help improve the quality of generated images and videos. The solution uses special language models that can learn from small amounts of data, which makes them useful for tasks like this one. The researchers tested their system and found that it worked much better than earlier methods. They also shared their dataset and trained model with the public. |
Keywords
* Artificial intelligence * Gemini * Gpt * Hallucination
