Summary of Multimodal Fusion and Coherence Modeling For Video Topic Segmentation, by Hai Yu et al.
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation
by Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu, Qian Chen, Wen Wang
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an improved approach to video topic segmentation (VTS), which is critical for efficient comprehension of video content and quick access to specific topics. Traditional methods struggle with accurately discerning topical transitions, while recent supervised approaches have achieved superior performance on action or scene segmentation. The proposed approach enhances multimodal fusion by exploring different architectures using cross-attention and mixture of experts, and pre-trains and fine-tunes the model with multimodal contrastive learning. A new pre-training task is also introduced for VTS, along with a novel fine-tuning task to enhance multimodal coherence modeling. The model is evaluated on educational videos and outperforms competitive unsupervised and supervised baselines on both English and Chinese lecture datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve video topic segmentation (VTS) by exploring new ways to combine different types of information from a video, like audio and text. VTS helps us quickly find specific parts of a video and understand what’s being talked about. Right now, most methods don’t do this very well. The authors suggest some new ideas for combining different types of information and testing them on educational videos like lectures. |
Keywords
» Artificial intelligence » Cross attention » Fine tuning » Mixture of experts » Supervised » Unsupervised
