@conference {39844,
	title = {Adaptive Prompt Learning for Blind Image Quality Assessment with Multi-modal Mixed-datasets Training},
	booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
	year = {2025},
	pages = {7453-7462},
	address = {Dublin, Ireland},
	abstract = {Due to the high cost and small scale of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent Blind IQA (BIQA) methods. Traditional deep learning-based methods emphasize visual information to capture quality features, while recent developments in Vision-Language Models (VLMs) demonstrate strong potential in learning generalizable representations through textual information. However, applying VLMs to BIQA poses three major Challenges: (1) How to make full use of the multi-modal information. (2) The prompt engineering for appropriate quality description is extremely time-consuming. (3) How to use mixed data for joint training to enhance the generalization of VLM-based BIQA model. To this end, we propose a Multi-modal BIQA method with prompt learning, named MMP-IQA. For (1), we propose a conditional fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, our model can capture quality information with a stronger representation ability. For (2), we model the quality prompt{\textquoteright}s context words with learnable vectors during the training process, which can be adaptively updated for superior performances. For (3), we jointly train a linearity-induced quality evaluator, a relative quality evaluator, and a dataset-specific absolute quality evaluator. In addition, we propose a dual automatic weight adjustment strategy to adaptively balance the loss weights between different datasets and among various losses within the same dataset. Extensive experiments illustrate the superior effectiveness of MMP-IQA.},
	url = {https://dl.acm.org/doi/abs/10.1145/3746027.3754896},
	author = {Yan Zhong and Xinping Zhao and Li Zhang and Xinyuan Song and Tingting Jiang}
}
