Personalized Image Aesthetics Assessment based on Graph Neural Network and Collaborative Filtering

Shi, Huiying; Guo, Jing; Ke, Yongzhen; Wang, Kai; Yang, Shuai; Qin, Fan; Chen, Liming

doi:10.1016/j.knosys.2024.111749

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2024.111749

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Personalized Image Aesthetics Assessment based on Graph Neural Network and Collaborative Filtering

Huiying Shi,

Jing Guo,

Yongzhen Ke

et al.

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Cited by 2 publications

References 27 publications

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MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Nie,

Huang,

Gao

et al. 2024

Knowledge-Based Systems

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MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Nie,

Huang,

Gao

et al. 2024

Knowledge-Based Systems

View full text Add to dashboard Cite

Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights

Wan,

Xiao,

et al. 2024

Symmetry

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The text-to-image task, a critical branch of computer vision and image processing, has witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI innovation. However, existing research often overlooks scenarios involving sparse textual input and fails to incorporate human personalized preferences into the generative process. To address these gaps, we propose a novel AI methodology: personalized short-text-to-image generation through aesthetic assessment and human insights. Our approach introduces a symmetry between personalized aesthetic preferences and the generated images by leveraging a data-driven personality encoder (PE) to extract personal information and embed it into a Big Five personality trait-based image aesthetic assessment (BFIAA) model. This model harmonizes aesthetic preferences with the generative process by adapting the stable diffusion framework to align with personalized assessments. Experimental results demonstrate the effectiveness of our method: the PE module achieves an accuracy of 98.1%, while the BFIAA model surpasses the baseline by 13% on the PLCC metric, accurately reflecting human aesthetic preferences. Furthermore, our adapted generation model improves convergence loss by over 10% compared to the base model, consistently producing personalized images that are more aligned with human preferences.

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Personalized Image Aesthetics Assessment based on Graph Neural Network and Collaborative Filtering

Cited by 2 publications

References 27 publications

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

MRAM: Multi-scale Regional Attribute-weighting via Meta-learning for Personalized Image Aesthetics Assessment

Imaginique Expressions: Tailoring Personalized Short-Text-to-Image Generation Through Aesthetic Assessment and Human Insights

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