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.
翻译：由于各种赌博技术、服务、游戏风格和平台的扩散,对赌博内容的多维审美评估对赌博行业越来越重要,根据不同游戏玩家、游戏设计者、图形开发者等在特定条件下的不同需要,需要多模式审美评估来考虑不同的审美层面/观点;由于不同审美层面之间有着不同的内在关系,例如“彩色”和“色彩和谐”之间有着不同的内在关系,因此利用多个相关层面的互补来源,可能有利于利用相关层面的有效信息。为此,我们通过多任务学习解决这一问题。我们的愿望是寻求和学习不同审美相关层面之间的相互关系,以进一步提高预测所有审美层面的通用性表现。因此,利用其他层面的互补来源,即通过共享不同层面的培训信息间接地增加培训数据,可能无法“瓶颈”地消除利用一个单个层面的有限标签数据进行良好预测,即通过共享不同层面的培训信息,从而增加培训数据。根据实验结果,拟议模型外形形形形图显示的四面的模型。
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