Video game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many of which are not concretely defined. Second, video game covers vary in many different ways such as colors, styles, textual information, etc, even for games of the same genre. Third, cover designs and textual descriptions may vary due to many external factors such as country, culture, target reader populations, etc. With the growing competitiveness in the video game industry, the cover designers and typographers push the cover designs to its limit in the hope of attracting sales. The computer-based automatic video game genre classification systems become a particularly exciting research topic in recent years. In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold. First, we compiles a large dataset consisting of 50,000 video games from 21 genres made of cover images, description text, and title text and the genre information. Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of genre classification for video games. Third, we developed an efficient and salable multi-modal framework based on both images and texts. Fourth, a thorough analysis of the experimental results is given and future works to improve the performance is suggested. The results show that the multi-modal framework outperforms the current state-of-the-art image-based or text-based models. Several challenges are outlined for this task. More efforts and resources are needed for this classification task in order to reach a satisfactory level.

中文翻译：

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视频游戏类型分类的深度学习

基于游戏封面和文字描述的视频游戏类型分类将完全有益于许多现代的标识，搭配和检索系统。同时，由于以下原因，这也是一项极具挑战性的任务：首先，存在各种各样的视频游戏类型，其中许多类型没有具体定义。其次，即使对于相同类型的游戏，视频游戏的封面也会以多种不同方式变化，例如颜色，样式，文本信息等。第三，封面设计和文字说明可能会因国家，文化，目标读者群体等许多外部因素而有所不同。随着视频游戏行业竞争的不断发展，封面设计者和印刷者将封面设计推向极限。希望吸引销售。近年来，基于计算机的自动视频游戏类型分类系统成为特别令人兴奋的研究课题。在本文中，我们提出了一种多模式深度学习框架来解决此问题。本文的贡献有四个方面。首先，我们编译一个大型数据集，该数据集由21种流派的50,000个视频游戏组成，这些游戏由封面图像，描述文本，标题文本和流派信息组成。其次，针对视频游戏的类型分类任务，全面评估了基于图像和基于文本的最新模型。第三，我们基于图像和文本开发了一种高效且可销售的多模式框架。第四，对实验结果进行了详尽的分析，并提出了改进性能的未来工作。结果表明，多模式框架的性能优于当前基于图像或文本的模型。概述了此任务的几个挑战。为了达到令人满意的水平，需要为该分类任务付出更多的努力和资源。