Towards Open-ended Visual Quality Comparison

• URL: http://arxiv.org/abs/2402.16641v2
• Date: Mon, 4 Mar 2024 07:06:22 GMT
• Title: Towards Open-ended Visual Quality Comparison
• Authors: 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, and Weisi Lin
• Abstract summary: We extend the edge of emerging large multi-modality models (LMMs) to advance visual quality comparison into open-ended settings. Co-Instruct is a first-of-its-kind open-source open-ended visual quality comparer. We demonstrate that Co-Instruct achieves in average 30% higher accuracy than state-of-the-art open-source LMMs.
• Score: 87.45004129101089
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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