Summary of Attention Prompting on Image For Large Vision-language Models, by Runpeng Yu and Weihao Yu and Xinchao Wang
Attention Prompting on Image for Large Vision-Language Models
by Runpeng Yu, Weihao Yu, Xinchao Wang
First submitted to arxiv on: 25 Sep 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 Attention Prompting on Image technique enhances Large Vision-Language Models’ (LVLMs) capabilities to perceive visual information by overlaying a text-query-guided attention heatmap on the original input image. This approach effectively improves LVLM performance on various vision-language tasks, such as image captioning and visual question answering. The technique generates an attention heatmap dependent on the text query using an auxiliary model like CLIP, which then multiplies the pixel values of the original image to obtain the actual input image for the LVLM. Experimental results demonstrate the effectiveness of Attention Prompting on Image, achieving significant improvements in benchmarks such as LLaVA-1.5, MM-Vet, and LLaVA-Wild. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help Large Vision-Language Models (LVLMs) understand what they’re looking at by adding special instructions called attention heatmaps onto the images they see. This helps them do better on tasks like describing pictures and answering questions about what’s in the image. The method uses another AI model, CLIP, to create these heatmaps based on the text instructions. It then combines this heatmap with the original image to give the LVLM a better understanding of what it’s looking at. The results show that this technique makes the LVLMs perform much better on several important benchmarks. |
Keywords
» Artificial intelligence » Attention » Image captioning » Prompting » Question answering
