Summary of Visualagentbench: Towards Large Multimodal Models As Visual Foundation Agents, by Xiao Liu et al.

VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents

by Xiao Liu, Tianjie Zhang, Yu Gu, Iat Long Iong, Yifan Xu, Xixuan Song, Shudan Zhang, Hanyu Lai, Xinyi Liu, Hanlin Zhao, Jiadai Sun, Xinyue Yang, Yu Yang, Zehan Qi, Shuntian Yao, Xueqiao Sun, Siyi Cheng, Qinkai Zheng, Hao Yu, Hanchen Zhang, Wenyi Hong, Ming Ding, Lihang Pan, Xiaotao Gu, Aohan Zeng, Zhengxiao Du, Chan Hee Song, Yu Su, Yuxiao Dong, Jie Tang

First submitted to arxiv on: 12 Aug 2024

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 The paper introduces VisualAgentBench (VAB), a comprehensive benchmark to evaluate Large Multimodal Models (LMMs) as visual foundation agents across diverse scenarios. The proposed benchmark tests LMMs’ understanding and interaction capabilities in tasks such as Embodied, Graphical User Interface, and Visual Design. By testing nine proprietary LMM APIs and eight open models, the study demonstrates the considerable yet still developing agent capabilities of these models. Additionally, VAB constructs a trajectory training set through hybrid methods to promote performance improvements in LMMs. The work aims to benchmark existing models and provide a foundation for future development into visual foundation agents.
Low	GrooveSquid.com (original content)	Low Difficulty Summary LMMs can do many things at once, like recognizing images and understanding text. They’re getting better all the time! But right now, there’s no easy way to test how good they are in different situations. That’s why scientists created VAB, a special tool that helps figure out what LMMs can really do. They tested nine special models and eight open models on this tool and found that these models are getting better at doing things like understanding pictures and interacting with humans.