Dynamic graph convolutional network for multi-video summarization

Jiaxin Wu, Sheng-hua Zhong, Yan Liu

Abstract: Highlights • The first work on dynamic graph convolutional network for multi-video summarization. • Two strategies are proposed to solve the class imbalance problem of the task. • A diversity regularization is designed to encourage a diverse summarization. • The proposed model can generate diverse summaries and achieve state-of-the-art performances. 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.