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.
@article{arxiv.2312.00220,
title = {Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation},
author = {Linzi Xing and Quan Tran and Fabian Caba and Franck Dernoncourt and Seunghyun Yoon and Zhaowen Wang and Trung Bui and Giuseppe Carenini},
journal= {arXiv preprint arXiv:2312.00220},
year = {2023}
}
Comments
Accepted at the 30th International Conference on Multimedia Modeling (MMM 2024)