MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware
  Multi-Segment Grounding
Authors: name: Fuwen Luo, name: Shengfeng Lou, name: Chi Chen, name: Ziyue Wang, name: Chenliang Li, name: Weizhou Shen, name: Jiyue Guo, name: Peng Li, name: Ming Yan, name: Ji Zhang, name: Fei Huang, name: Yang Liu

Video temporal understanding is crucial for multimodal large language models
(MLLMs) to reason over events in videos. Despite recent advances in general
video understanding, current MLLMs still struggle with fine-grained temporal
reasoning. While reinforcement learning (RL) has been explored to address this
issue recently, existing RL approaches remain limited in effectiveness. In this
work, we propose MUSEG, a novel RL-based method that enhances temporal
understanding by introducing timestamp-aware multi-segment grounding. MUSEG
enables MLLMs to align queries with multiple relevant video segments, promoting
more comprehensive temporal reasoning. To facilitate effective learning, we
design a customized RL training recipe with phased rewards that progressively
guides the model toward temporally grounded reasoning. Extensive experiments on
temporal grounding and time-sensitive video QA tasks demonstrate that MUSEG
significantly outperforms existing methods and generalizes well across diverse
temporal understanding scenarios. View our project at
https://github.com/THUNLP-MT/MUSEG.