  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.
