TY  - EJOU
AU  - Zhang, Baiyan 
AU  - Ling, Hefei 
AU  - Li, Ping 
AU  - Wang, Qian 
AU  - Shi, Yuxuan 
AU  - Wu, Lei 
AU  - Wang, Runsheng 
AU  - Shen, Jialie 

TI  - Multi-Head Attention Graph Network for Few Shot Learning
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 68
IS  - 2
SN  - 1546-2226

AB  - 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 the update direction. Our proposed model is verified through extensive experiments on two few-shot benchmark MiniImageNet and CIFAR-FS dataset. The results suggest that our method has a strong capability of noise immunity and quick convergence. The classification accuracy outperforms most state-of-the-art approaches.
KW  - Few shot learning; attention; graph network

DO  - 10.32604/cmc.2021.016851