HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment
Zhichao Liao, Xiaokun Liu, Wenyu Qin, Qingyu Li, Qiulin Wang, Pengfei Wan, Di Zhang, Long Zeng, Pingfa Feng·March 31, 2025
Summary
The HumanAesExpert framework features the HumanBeauty dataset for high-quality aesthetic annotations and a Vision Language Model, HumanAesExpert, excelling in overall and detailed assessments. It comprises an Expert head for integrating human knowledge and MetaVoter for score aggregation, surpassing current methods. Recent advancements in text-to-image generation, computer vision, and pattern recognition are highlighted, with models like Dreamfit, Deepseek-v3, Llava-next, Swin Transformer, and Llama-3.2-11b-vision discussed. Contributions by Kelvin CK Chan and Chen Change Loy focus on "clip" for image assessment. Research papers emphasize text-to-video model alignment, low-level vision tasks, and multi-modality foundation models. Contributions include "Lift," "Q-bench," and "Q-instruct," aiming to enhance vision-language models' world perception. Studies also cover image aesthetic quality assessment and scalable infrastructure for fine-tuning.
Introduction
Background
Overview of aesthetic assessment challenges
Importance of high-quality aesthetic annotations
Objective
To introduce HumanAesExpert framework and its components
Highlight advancements in text-to-image generation, computer vision, and pattern recognition
HumanAesExpert Framework
Core Components
HumanBeauty Dataset: High-quality aesthetic annotations for diverse image categories
Vision Language Model (HumanAesExpert): Capabilities for overall and detailed aesthetic assessments
Expert Head
Integration of human knowledge for enhanced assessment
MetaVoter
Score aggregation mechanism surpassing current methods
Recent Advancements
Text-to-Image Generation
Dreamfit, Deepseek-v3, Llava-next, Swin Transformer, Llama-3.2-11b-vision
Research Contributions
Kelvin CK Chan and Chen Change Loy on "clip" for image assessment
Research Papers
Focus on text-to-video model alignment, low-level vision tasks, and multi-modality foundation models
Contributions
Lift, Q-bench, Q-instruct for enhancing vision-language models' world perception
Image Aesthetic Quality Assessment
Scalable Infrastructure
Fine-tuning for improved model performance
Research Focus
Methodologies for scalable and accurate aesthetic quality assessment
Conclusion
Future Directions
Potential improvements and future research areas
Impact
Contribution to the field of computer vision and pattern recognition
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What are the main components of the HumanAesExpert framework and how do they contribute to its performance?
What innovative contributions do 'Lift,' 'Q-bench,' and 'Q-instruct' make to vision-language models?
How does the Vision Language Model, HumanAesExpert, integrate human knowledge for aesthetic assessments?
In what ways do models like Dreamfit, Deepseek-v3, and Swin Transformer enhance the capabilities of HumanAesExpert?