MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
This work addresses a critical bottleneck in video temporal understanding for MLLMs, offering a novel approach to enhance event reasoning, though it appears incremental as it builds on existing RL methods.
The paper tackles the problem of fine-grained temporal reasoning in video understanding for multimodal large language models (MLLMs) by proposing MUSEG, a reinforcement learning-based method that uses timestamp-aware multi-segment grounding, resulting in significant performance improvements on temporal grounding and time-sensitive video QA tasks.
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.