Jing Zhang*, Guangli Wu, Shanshan Song
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1947-1965, 2024, DOI:10.32604/cmc.2024.051781
Abstract Video summarization aims to select key frames or key shots to create summaries for fast retrieval, compression, and efficient browsing of videos. Graph neural networks efficiently capture information about graph nodes and their neighbors, but ignore the dynamic dependencies between nodes. To address this challenge, we propose an innovative Adaptive Graph Convolutional Adjacency Matrix Network (TAMGCN), leveraging the attention mechanism to dynamically adjust dependencies between graph nodes. Specifically, we first segment shots and extract features of each frame, then compute the representative features of each shot. Subsequently, we utilize the attention mechanism to dynamically adjust More >
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