Summary of Searchlvlms: a Plug-and-play Framework For Augmenting Large Vision-language Models by Searching Up-to-date Internet Knowledge, By Chuanhao Li et al.
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge
by Chuanhao Li, Zhen Li, Chenchen Jing, Shuo Liu, Wenqi Shao, Yuwei Wu, Ping Luo, Yu Qiao, Kaipeng Zhang
First submitted to arxiv on: 23 May 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 A large vision-language model (LVLM) limitation is its inability to stay updated with recent knowledge due to resource constraints, leading to failures in various scenarios. For instance, a LVLM released in January 2024 would be unaware of the Detective Conan movie theme song singer, which was revealed in April 2024. To address this issue, retrieval-augmented generation (RAG) motivates providing LVLMs with up-to-date knowledge through internet search during inference, also found in commercial LVLMs like GPT-4V. However, these mechanics remain unclear. This paper proposes a plug-and-play framework, SearchLVLMs, to augment existing LVLMs for visual question answering (VQA) about recent knowledge. A hierarchical filtering model is trained to efficiently retrieve helpful content from search engine results to prompt LVLMs with updated information. The paper also presents a pipeline for automatically generating news-related VQA samples and introduces a multi-model voting mechanism to label the usefulness of website/content for training. Experimental results demonstrate SearchLVLMs’ effectiveness, outperforming GPT-4V by approximately 25% in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models have limitations when it comes to staying updated with recent knowledge. They can’t learn new things easily because they need a lot of resources. This makes them fail in many situations. For example, if an LVLM was released in January and didn’t know the singer of a new movie’s theme song, which wasn’t released until April. To solve this problem, researchers propose using internet search during inference to provide LVLMs with up-to-date knowledge. They also want to improve how LVLMs answer visual questions about recent events. The paper presents a way to do this by training a model to find helpful content online and use it to prompt LVLMs. |
Keywords
» Artificial intelligence » Gpt » Inference » Language model » Prompt » Question answering » Rag » Retrieval augmented generation
