Hierarchical Description Learning for Artistic Image Aesthetics Assessment
Published:Dec 29, 2025 12:18
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of aesthetic quality assessment for AI-generated content (AIGC). It tackles the issues of data scarcity and model fragmentation in this complex task. The authors introduce a new dataset (RAD) and a novel framework (ArtQuant) to improve aesthetic assessment, aiming to bridge the cognitive gap between images and human judgment. The paper's significance lies in its attempt to create a more human-aligned evaluation system for AIGC, which is crucial for the development and refinement of AI art generation.
Key Takeaways
- •Addresses data scarcity and model fragmentation in aesthetic assessment.
- •Introduces the Refined Aesthetic Description (RAD) dataset.
- •Proposes the ArtQuant framework for improved aesthetic evaluation.
- •Achieves state-of-the-art performance with reduced training epochs.
- •Aims to bridge the cognitive gap between artistic images and aesthetic judgment.
Reference
“The paper introduces the Refined Aesthetic Description (RAD) dataset and the ArtQuant framework, achieving state-of-the-art performance while using fewer training epochs.”