Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment
Henglin Liu, Nisha Huang, Chang Liu, Jiangpeng Yan, Huijuan Huang, Jixuan Ying, Tong-Yee Lee, Pengfei Wan
Abstract
The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature—spanning visual perception, cognition, and emotion—poses fundamental challenges. Although aesthetic descriptions offer a viable representation of this complexity, two critical challenges persist: (1) data scarcity and imbalance: existing dataset overly focuses on visual perception and neglects deeper dimensions due to the expensive manual annotation; and (2) model fragmentation: current visual networks isolate aesthetic attributes with multi-branch encoder, while multimodal methods represented by contrastive learning struggle to effectively process long-form textual descriptions. To resolve challenge (1), we first present the Refined Aesthetic Description (RAD) dataset, a large-scale (70k), multi-dimensional structured dataset, generated via an iterative pipeline without heavy annotation costs and easy to scale. To address challenge (2), we propose ArtQuant, an aesthetics assessment framework for artistic image which not only couple isolated aesthetic dimensions through joint description generation, but also better model long-text semantics with the help of LLM decoders. Besides, theoretical analysis confirms this symbiosis: RAD
BibTeX
@inproceedings{aaai2026_bridgingcognitiv,
title = {Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment},
author = {Henglin Liu and Nisha Huang and Chang Liu and Jiangpeng Yan and Huijuan Huang and Jixuan Ying and Tong-Yee Lee and Pengfei Wan and Xiangyang Ji},
booktitle = {AAAI 2026},
year = {2026}
}