3m-Game: Multi-Modal Multi-Task Multi-Teacher Learning for Game Event Detection (Student Abstract)
Abstract
Esports has rapidly emerged as a global phenomenon with an ever-expanding audience on livestream platforms. However, due to the complex nature of the game, it becomes challenging for newcomers to comprehend the gaming situation. This research introduces a 3M-Game that integrates multi-modal (MM) information from the livestream platform, including chat and livestream, to uncover the event. While conventional MM models typically prioritise aligning MM data through concurrent training towards a unified objective, our framework leverages multiple independent teachers trained on different tasks to accomplish game event detection. The results show the effectiveness of the proposed framework. The code and appendix are in https://github.com/adlnlp/3m_game.
Cite
Text
Ng et al. "3m-Game: Multi-Modal Multi-Task Multi-Teacher Learning for Game Event Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35283Markdown
[Ng et al. "3m-Game: Multi-Modal Multi-Task Multi-Teacher Learning for Game Event Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/ng2025aaai-m/) doi:10.1609/AAAI.V39I28.35283BibTeX
@inproceedings{ng2025aaai-m,
title = {{3m-Game: Multi-Modal Multi-Task Multi-Teacher Learning for Game Event Detection (Student Abstract)}},
author = {Ng, Thye Shan and Cao, Feiqi and Han, Soyeon Caren},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29448-29450},
doi = {10.1609/AAAI.V39I28.35283},
url = {https://mlanthology.org/aaai/2025/ng2025aaai-m/}
}