Multi-Modal Aesthetic Assessment for MObile Gaming Image

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 performance in predicting all the aesthetic dimensions. Therefore, the `bottleneck' of obtaining good predictions with limited labeled data for one individual dimension could be unplugged by harnessing complementary sources of other dimensions, i.e., augment the training data indirectly by sharing training information across dimensions. According to experimental results, the proposed model outperforms state-of-the-art aesthetic metrics significantly in predicting four gaming aesthetic dimensions.