  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.
