Esports has rapidly emerged as a global phenomenon with an ever-expanding audience via platforms, like YouTube. Due to the inherent complexity nature of the game, it is challenging for newcomers to comprehend what the event entails. The chaotic nature of online chat, the fast-paced speech of the game commentator, and the game-specific user interface further compound the difficulty for users in comprehending the gameplay. To overcome these challenges, it is crucial to integrate the Multi-Modal (MM) information from the platform and understand the event. The paper introduces a new MM multi-teacher-based game event detection framework, with the ultimate goal of constructing a comprehensive framework that enhances the comprehension of the ongoing game situation. While conventional MM models typically prioritise aligning MM data through concurrent training towards a unified objective, our framework leverages multiple teachers trained independently on different tasks to accomplish the Game Event Detection. The experiment clearly shows the effectiveness of the proposed MM multi-teacher framework.

该研究提出了一种新型的多模信息游戏事件检测框架，旨在构建一个综合框架，增强对进行中游戏情况的理解。实验证明了所提出的多模信息多教师框架的有效性。

3M：多模态多任务多教师学习用于游戏事件检测