Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment

• URL: http://arxiv.org/abs/2406.16641v1
• Date: Mon, 24 Jun 2024 13:45:31 GMT
• Title: Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment
• Authors: Jun Fu, Wei Zhou, Qiuping Jiang, Hantao Liu, Guangtao Zhai,
• Abstract summary: We propose vision-language consistency guided multi-modal prompt learning for blind image quality assessment (AGIQA) Specifically, we introduce learnable textual and visual prompts in language and vision branches of Contrastive Language-Image Pre-training (CLIP) models. 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.
• Score: 57.07360640784803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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