Baiyan Zhang1, Hefei Ling1,*, Ping Li1, Qian Wang1, Yuxuan Shi1, Lei Wu1, Runsheng Wang1, Jialie Shen2
CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1505-1517, 2021, DOI:10.32604/cmc.2021.016851
Abstract The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise of irrelevant nodes and optimize… More >
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