Summary of Large Multimodal Agents: a Survey, by Junlin Xie and Zhihong Chen and Ruifei Zhang and Xiang Wan and Guanbin Li
Large Multimodal Agents: A Survey
by Junlin Xie, Zhihong Chen, Ruifei Zhang, Xiang Wan, Guanbin Li
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have revolutionized text-based AI agents, enabling them to make decisions and reason like humans. To take this technology further, researchers are now focusing on extending these agents into the multimodal domain, where they can interpret and respond to diverse queries from various sources. This paper reviews the current state of large multimodal agents (LMAs), which combine language models with other modalities such as vision or audio. We categorize existing research into four types based on the essential components involved in developing LMAs and explore collaborative frameworks that integrate multiple LMAs. A major challenge in this field is the lack of standardized evaluation methods, making it difficult to compare different LMAs. To address this issue, we compile various evaluation methodologies and propose a comprehensive framework for bridging the gaps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine AI agents that can understand and respond to your voice, texts, or images. Researchers are working on creating these agents, which they call large multimodal agents (LMAs). They combine language models with other sources of information like pictures or sounds. This paper looks at what’s happening in this field right now. It groups the different approaches into categories and shows how some researchers are working together to make their AI agents better. One problem is that each researcher uses a different way to test their agent, making it hard to compare them. To fix this, the authors suggest ways to evaluate these agents fairly. |
