Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted
significant attention due to the growing demand for video analysis. Recent
approaches treat MR and HD as similar video grounding problems and address them
together with transformer-based architecture. However, we observe that the
emphasis of MR and HD differs, with one necessitating the perception of local
relationships and the other prioritizing the understanding of global contexts.
Consequently, the lack of task-specific design will inevitably lead to
limitations in associating the intrinsic specialty of two tasks. To tackle the
issue, we propose a Unified Video COMprehension framework (UVCOM) to bridge the
gap and jointly solve MR and HD effectively. By performing progressive
integration on intra and inter-modality across multi-granularity, UVCOM
achieves the comprehensive understanding in processing a video. Moreover, we
present multi-aspect contrastive learning to consolidate the local relation
modeling and global knowledge accumulation via well aligned multi-modal space.
Extensive experiments on QVHighlights, Charades-STA, TACoS , YouTube Highlights
and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which
outperforms the state-of-the-art methods by a remarkable margin.

提出了一种统一视频理解框架 (UVCOM)，通过深入挖掘内在的本质特点，联合解决视频片段检索和重点片段检测问题。在多粒度的内部和跨模态的进步集成中，通过多方面对比学习，实现了对视频进行全面理解，成功地提高了效果。