Multimodal Fusion and Coherence Modeling for Video Topic Segmentation

2024-08-01

arXiv.org

AI Summary

Video Topic Segmentation (VTS) for educational lecture videos remains challenging due to the need for semantically coherent boundary detection across multimodal signals. Method: This paper proposes a semantic-boundary-aware multimodal modeling paradigm, featuring a novel video–text alignment self-supervised pretraining task, cross-modal cross-attention, a Mixture-of-Experts (MoE) architecture, and fine-grained multimodal contrastive learning to jointly model speech, text, and visual features with consistency. Contribution/Results: We introduce LecVid, the first large-scale Chinese lecture video benchmark dataset. Experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised and supervised baselines on both Chinese and English educational video datasets, achieving new SOTA accuracy and boundary F1 scores for topic segmentation—thereby enhancing video content understanding and key segment localization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The video topic segmentation (VTS) task segments videos into intelligible, non-overlapping topics, facilitating efficient comprehension of video content and quick access to specific content. VTS is also critical to various downstream video understanding tasks. Traditional VTS methods using shallow features or unsupervised approaches struggle to accurately discern the nuances of topical transitions. Recently, supervised approaches have achieved superior performance on video action or scene segmentation over unsupervised approaches. In this work, we improve supervised VTS by thoroughly exploring multimodal fusion and multimodal coherence modeling. Specifically, (1) we enhance multimodal fusion by exploring different architectures using cross-attention and mixture of experts. (2) To generally strengthen multimodality alignment and fusion, we pre-train and fine-tune the model with multimodal contrastive learning. (3) We propose a new pre-training task tailored for the VTS task, and a novel fine-tuning task for enhancing multimodal coherence modeling for VTS. We evaluate the proposed approaches on educational videos, in the form of lectures, due to the vital role of topic segmentation of educational videos in boosting learning experiences. Additionally, we introduce a large-scale Chinese lecture video dataset to augment the existing English corpus, promoting further research in VTS. Experiments on both English and Chinese lecture datasets demonstrate that our model achieves superior VTS performance compared to competitive unsupervised and supervised baselines.

Problem

• Video Theme Segmentation
• Content Understanding
• Lecture Videos

Innovation

• Multimodal Fusion
• Cross Attention
• Expert Mixture Architecture