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arXiv · 2410.09377

GEM-VPC: A dual Graph-Enhanced Multimodal integration for Video Paragraph Captioning

Eileen Wang·Caren Han·Josiah Poon
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Abstract

Video Paragraph Captioning (VPC) aims to generate paragraph captions that summarises key events within a video. Despite recent advancements, challenges persist, notably in effectively utilising multimodal signals inherent in videos and addressing the long-tail distribution of words. The paper introduces a novel multimodal integrated caption generation framework for VPC that leverages information from various modalities and external knowledge bases. Our framework constructs two graphs: a 'video-specific' temporal graph capturing major events and interactions between multimodal information and commonsense knowledge, and a 'theme graph' representing correlations between words of a specific theme. These graphs serve as input for a transformer network with a shared encoder-decoder architecture. We also introduce a node selection module to enhance decoding efficiency by selecting the most relevant nodes from the graphs. Our results demonstrate superior performance across benchmark datasets.

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Eileen Wang, Caren Han, Josiah Poon. 2024-10-12. GEM-VPC: A dual Graph-Enhanced Multimodal integration for Video Paragraph Captioning. https://arxiv.org/abs/2410.09377

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