Original title: LiveChat: Video Comment Generation from Audio-Visual Multimodal Contexts
Authors: Julien Lalanne, Raphael Bournet, Yi Yu
In their article, researchers delve into the world of live video commenting, a vibrant space where viewers engage with content through comments, reactions, and questions. They highlight the challenge for AI: understanding live audio-visual streams and engaging in dialogue with human viewers. Due to limited datasets, they create a vast audio-visual dialogue repository from Twitch, offering 11 diverse categories across 575 streamers, totaling 438 hours and 3.2 million comments. Introducing a novel multimodal model, they aim to generate live comments synchronized with video events and ongoing dialogues. Initial results showcase the model’s effectiveness, laying a strong foundation for enhancing live video interactions through AI. This work not only expands datasets but also promises practical applications, steering advancements in live video engagement technology.
Original article: https://arxiv.org/abs/2311.12826