2AFC Prompting of Large Multimodal Models for Image Quality Assessment
<|reference_start|>2AFC Prompting of Large Multimodal Models for Image Quality Assessment: While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.<|reference_end|>
@article{zhu20242afc,
    title={2AFC Prompting of Large Multimodal Models for Image Quality Assessment},
    author={Hanwei Zhu, Xiangjie Sui, Baoliang Chen, Xuelin Liu, Peilin Chen,
  Yuming Fang, and Shiqi Wang},
    journal={arXiv preprint arXiv:2402.01162},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01162},
    primaryClass={cs.CV cs.AI}
}