  Recently, textual prompt tuning has shown inspirational performance in
adapting Contrastive Language-Image Pre-training (CLIP) models to natural image
quality assessment. However, such uni-modal prompt learning method only tunes
the language branch of CLIP models. This is not enough for adapting CLIP models
to AI generated image quality assessment (AGIQA) since AGIs visually differ
from natural images. In addition, the consistency between AGIs and user input
text prompts, which correlates with the perceptual quality of AGIs, is not
investigated to guide AGIQA. In this letter, we propose vision-language
consistency guided multi-modal prompt learning for blind AGIQA, dubbed
CLIP-AGIQA. Specifically, we introduce learnable textual and visual prompts in
language and vision branches of CLIP models, respectively. Moreover, we design
a text-to-image alignment quality prediction task, whose learned
vision-language consistency knowledge is used to guide the optimization of the
above multi-modal prompts. Experimental results on two public AGIQA datasets
demonstrate that the proposed method outperforms state-of-the-art quality
assessment models. The source code is available at
https://github.com/JunFu1995/CLIP-AGIQA.
