Multi-Modal Aesthetic Assessment for MObile Gaming Image
Zhenyu Lei, Yejing Xie, Suiyi Ling, Andreas Pastor, Junle Wang,, Patrick Le Callet

TL;DR
This paper introduces a multi-task learning model for multi-modal aesthetic assessment of gaming images, leveraging correlations between aesthetic dimensions to improve prediction accuracy across diverse gaming content.
Contribution
It proposes a novel multi-task learning approach that exploits relationships between aesthetic dimensions to enhance predictive performance with limited labeled data.
Findings
Outperforms state-of-the-art aesthetic metrics in four gaming aesthetic dimensions
Effectively leverages correlations between aesthetic dimensions to improve generalization
Demonstrates significant improvement in predicting gaming image aesthetics
Abstract
With the proliferation of various gaming technology, services, game styles, and platforms, multi-dimensional aesthetic assessment of the gaming contents is becoming more and more important for the gaming industry. Depending on the diverse needs of diversified game players, game designers, graphical developers, etc. in particular conditions, multi-modal aesthetic assessment is required to consider different aesthetic dimensions/perspectives. Since there are different underlying relationships between different aesthetic dimensions, e.g., between the `Colorfulness' and `Color Harmony', it could be advantageous to leverage effective information attached in multiple relevant dimensions. To this end, we solve this problem via multi-task learning. Our inclination is to seek and learn the correlations between different aesthetic relevant dimensions to further boost the generalization…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Aesthetic Perception and Analysis · Creativity in Education and Neuroscience
