MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding

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 atthis https URL.

@article{luo2025_2505.20715,
  title={ MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding },
  author={ Fuwen Luo and Shengfeng Lou and Chi Chen and Ziyue Wang and Chenliang Li and Weizhou Shen and Jiyue Guo and Peng Li and Ming Yan and Ji Zhang and Fei Huang and Yang Liu },
  journal={arXiv preprint arXiv:2505.20715},
  year={ 2025 }
}