OIG-Bench: A Multi-Agent Annotated Benchmark for Multimodal One-Image Guides Understanding

• URL: http://arxiv.org/abs/2510.00069v1
• Date: Mon, 29 Sep 2025 15:44:08 GMT
• Title: OIG-Bench: A Multi-Agent Annotated Benchmark for Multimodal One-Image Guides Understanding
• Authors: Jiancong Xie, Wenjin Wang, Zhuomeng Zhang, Zihan Liu, Qi Liu, Ke Feng, Zixun Sun, Yuedong Yang,
• Abstract summary: We present OIG-Bench, a benchmark focused on One-Image Guide understanding across diverse domains.<n>We have conducted a comprehensive evaluation of 29 state-of-the-art MLLMs, including both proprietary and open-source models.<n>Results show that Qwen2.5-VL-72B performs the best among the evaluated models, with an overall accuracy of 77%.
• Score: 26.45873982159107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities. However, evaluating their capacity for human-like understanding in One-Image Guides remains insufficiently explored. One-Image Guides are a visual format combining text, imagery, and symbols to present reorganized and structured information for easier comprehension, which are specifically designed for human viewing and inherently embody the characteristics of human perception and understanding. Here, we present OIG-Bench, a comprehensive benchmark focused on One-Image Guide understanding across diverse domains. To reduce the cost of manual annotation, we developed a semi-automated annotation pipeline in which multiple intelligent agents collaborate to generate preliminary image descriptions, assisting humans in constructing image-text pairs. With OIG-Bench, we have conducted a comprehensive evaluation of 29 state-of-the-art MLLMs, including both proprietary and open-source models. The results show that Qwen2.5-VL-72B performs the best among the evaluated models, with an overall accuracy of 77%. Nevertheless, all models exhibit notable weaknesses in semantic understanding and logical reasoning, indicating that current MLLMs still struggle to accurately interpret complex visual-text relationships. In addition, we also demonstrate that the proposed multi-agent annotation system outperforms all MLLMs in image captioning, highlighting its potential as both a high-quality image description generator and a valuable tool for future dataset construction. Datasets are available at https://github.com/XiejcSYSU/OIG-Bench.

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