  Video topic segmentation unveils the coarse-grained semantic structure
underlying videos and is essential for other video understanding tasks. Given
the recent surge in multi-modal, relying solely on a single modality is
arguably insufficient. On the other hand, prior solutions for similar tasks
like video scene/shot segmentation cater to short videos with clear visual
shifts but falter for long videos with subtle changes, such as livestreams. In
this paper, we introduce a multi-modal video topic segmenter that utilizes both
video transcripts and frames, bolstered by a cross-modal attention mechanism.
Furthermore, we propose a dual-contrastive learning framework adhering to the
unsupervised domain adaptation paradigm, enhancing our model's adaptability to
longer, more semantically complex videos. Experiments on short and long video
corpora demonstrate that our proposed solution, significantly surpasses
baseline methods in terms of both accuracy and transferability, in both intra-
and cross-domain settings.
