Towards Open-ended Visual Quality Comparison

Haoning Wu; Hanwei Zhu; Zicheng Zhang; Erli Zhang; Chaofeng Chen; Liang Liao; Chunyi Li; Annan Wang; Wenxiu Sun; Qiong Yan; Xiaohong Liu; Guangtao Zhai; Shiqi Wang; Weisi Lin

arXiv · 2402.16641

Towards Open-ended Visual Quality Comparison

Haoning Wu·Hanwei Zhu·Zicheng Zhang·Erli Zhang·Chaofeng Chen·Liang Liao·Chunyi Li·Annan Wang·Wenxiu Sun·Qiong Yan·Xiaohong Liu·Guangtao Zhai·Shiqi Wang·Weisi Lin

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Abstract

Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses. In this work, we extend the edge of emerging large multi-modality models (LMMs) to further advance visual quality comparison into open-ended settings, that 1) can respond to open-range questions on quality comparison; 2) can provide detailed reasonings beyond direct answers. To this end, we propose the Co-Instruct. To train this first-of-its-kind open-source open-ended visual quality comparer, we collect the Co-Instruct-562K dataset, from two sources: (a) LLM-merged single image quality description, (b) GPT-4V "teacher" responses on unlabeled data. Furthermore, to better evaluate this setting, we propose the MICBench, the first benchmark on multi-image comparison for LMMs. We demonstrate that Co-Instruct not only achieves in average 30% higher accuracy than state-of-the-art open-source LMMs, but also outperforms GPT-4V (its teacher), on both existing related benchmarks and the proposed MICBench. Our model is published at https://huggingface.co/q-future/co-instruct.

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Haoning Wu, Hanwei Zhu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Annan Wang, Wenxiu Sun, Qiong Yan, Xiaohong Liu, Guangtao Zhai, Shiqi Wang, Weisi Lin. 2024-02-26. Towards Open-ended Visual Quality Comparison. https://arxiv.org/abs/2402.16641

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