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

• URL: http://arxiv.org/abs/2410.09377v1
• Date: Sat, 12 Oct 2024 06:01:00 GMT
• Title: GEM-VPC: A dual Graph-Enhanced Multimodal integration for Video Paragraph Captioning
• Authors: Eileen Wang, Caren Han, Josiah Poon,
• Abstract summary: Video paragraph Captioning (VPC) aims to generate paragraph captions that summarises key events within a video. 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. Results demonstrate superior performance across benchmark datasets.
• Score: 4.290482766926506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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