Dynamic graph convolutional network for multi-video summarization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Article number107382
Journal / PublicationPattern Recognition
Volume107
Online published20 Jun 2020
Publication statusOnline published - 20 Jun 2020

Abstract

Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.

Research Area(s)

  • Class imbalance problem, Graph convolutional network, Multi-video summarization