Hierarchical Prompt Engineering and Task-Differentiated Low-Rank Adaptation for Artificial Intelligence-Generated Content Image Quality Assessment
Year: 2025
Type: article
Abstract: Assessing the quality of Artificial Intelligence-Generated Content (AIGC) images remains a critical challenge, as conventional Image Quality Assessment (IQA) methods often fail to capture the semantic consistency between generated images and their textual prompts. This study aims to establish an interpretable and efficient multimodal framework for evaluating AIGC image quality. The research addresses three key scientific questions: how to leverage structured prompt semantics for more interpretable assessments, how to enable parameter-efficient yet accurate adaptation, and how to achieve unified handling of perceptual and semantic subtasks. To this end, we propose the Prompt-Enhanced Low-Rank Adaptation (PELA) framework, which integrates Hierarchical Prompt Engineering and Low-Rank Adaptation within a CLIP-based backbone. Hierarchical prompts encode multi-level semantics for fine-grained evaluation, while low-rank adaptation enables lightweight, task-specific optimization. Experiments conducted on AGIQA-1K, AGIQA-3K, and AIGCIQA-2023 datasets demonstrate that PELA achieves superior correlation with human perceptual judgments and sets new state-of-the-art results across multiple metrics. The findings confirm that combining structured prompt semantics with efficient adaptation offers a compact, interpretable, and scalable paradigm for multimodal image quality assessment.
Source: Information
DOI: 10.3390/info16111006
Authors: Minjuan Gao, Qiaorong Zhang, Chenye Song, Xuande Zhang, Yankang Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Funder: Taiyuan Normal University
OpenAlex ID: W7106027402