Multi-Modal Machine Learning for Assessing Gaming Skills in Online
  Streaming: A Case Study with CS:GO
Longxiang Zhang Wenping Wang
Online streaming is an emerging market that address much attention. Assessing
gaming skills from videos is an important task for streaming service providers
to discover talented gamers. Service providers require the information to offer
customized recommendation and service promotion to their customers. Meanwhile,
this is also an important multi-modal machine learning tasks since online
streaming combines vision, audio and text modalities. In this study we begin by
identifying flaws in the dataset and proceed to clean it manually. Then we
propose several variants of latest end-to-end models to learn joint
representation of multiple modalities. Through our extensive experimentation,
we demonstrate the efficacy of our proposals. Moreover, we identify that our
proposed models is prone to identifying users instead of learning meaningful
representations. We purpose future work to address the issue in the end.