Summary of Hammr: Hierarchical Multimodal React Agents For Generic Vqa, by Lluis Castrejon et al.
HAMMR: HierArchical MultiModal React agents for generic VQA
by Lluis Castrejon, Thomas Mensink, Howard Zhou, Vittorio Ferrari, Andre Araujo, Jasper Uijlings
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper presents a unified framework for solving multimodal tasks such as Visual Question Answering (VQA) using Large Language Models (LLMs) combined with external specialized tools (LLMs+tools). The authors demonstrate the limitations of naively applying this approach and introduce HAMMR: HierArchical MultiModal React, which enables agents to call upon other specialized agents for enhanced compositionality. This hierarchical approach outperforms the naive LLM+tools approach by 19.5% on a generic VQA suite, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big language models with other tools to solve problems that involve both text and images, like answering questions based on pictures. The authors tried using this approach without optimizing it for each specific task and found it didn’t work well. So, they created a new way of combining these models called HAMMR, which lets different agents work together to help with the problem. This new method works much better than the old one and even beats other top-performing methods on some tasks. |
Keywords
» Artificial intelligence » Question answering
