This paper presents a summary of the VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models (LMMs), hosted as part of the ICCV 2025 Workshop on Visual Quality Assessment. The challenge aims to evaluate and enhance the ability of state-of-the-art LMMs to perform open-ended and detailed reasoning about visual quality differences across multiple images. To this end, the competition introduces a novel benchmark comprising thousands of coarse-to-fine grained visual quality comparison tasks, spanning single images, pairs, and multi-image groups. Each task requires models to provide accurate quality judgments. The competition emphasizes holistic evaluation protocols, including 2AFC-based binary preference and multi-choice questions (MCQs). Around 100 participants submitted entries, with five models demonstrating the emerging capabilities of instruction-tuned LMMs on quality assessment. This challenge marks a significant step toward open-domain visual quality reasoning and comparison and serves as a catalyst for future research on interpretable and human-aligned quality evaluation systems.
@article{arxiv.2509.09190,
title = {VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results},
author = {Hanwei Zhu and Haoning Wu and Zicheng Zhang and Lingyu Zhu and Yixuan Li and Peilin Chen and Shiqi Wang and Chris Wei Zhou and Linhan Cao and Wei Sun and Xiangyang Zhu and Weixia Zhang and Yucheng Zhu and Jing Liu and Dandan Zhu and Guangtao Zhai and Xiongkuo Min and Zhichao Zhang and Xinyue Li and Shubo Xu and Anh Dao and Yifan Li and Hongyuan Yu and Jiaojiao Yi and Yiding Tian and Yupeng Wu and Feiran Sun and Lijuan Liao and Song Jiang},
journal= {arXiv preprint arXiv:2509.09190},
year = {2025}
}