Summary of Vision Search Assistant: Empower Vision-language Models As Multimodal Search Engines, by Zhixin Zhang et al.
Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines
by Zhixin Zhang, Yiyuan Zhang, Xiaohan Ding, Xiangyu Yue
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 Vision Search Assistant framework enables large vision-language models (VLMs) to retrieve reliable answers for novel visual content, such as identifying an object not previously seen. This is achieved by leveraging VLMs’ visual understanding capabilities and web agents’ real-time information access for open-world Retrieval-Augmented Generation via the web. The framework integrates visual and textual representations, allowing informed responses even when the image is novel to the system. Experimental results on both open-set and closed-set QA benchmarks demonstrate that Vision Search Assistant outperforms other models and can be applied to existing VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Vision Search Assistant helps computers understand new images better. Currently, computers struggle to find answers about objects or events they’ve never seen before. This is because their “eyes” are trained on specific things and don’t know what else might exist. To fix this, researchers created a new system that combines the computer’s understanding of images with real-time information from the internet. This allows the computer to provide good answers even when it doesn’t recognize something. The new system works better than previous ones and can be used with many different computer models. |
Keywords
» Artificial intelligence » Retrieval augmented generation
