@Article{cmc.2021.016851,
AUTHOR = {Baiyan Zhang, Hefei Ling, Ping Li, Qian Wang, Yuxuan Shi, Lei Wu, Runsheng Wang, Jialie Shen},
TITLE = {Multi-Head Attention Graph Network for Few Shot Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {68},
YEAR = {2021},
NUMBER = {2},
PAGES = {1505--1517},
URL = {http://www.techscience.com/cmc/v68n2/42197},
ISSN = {1546-2226},
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 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.},
DOI = {10.32604/cmc.2021.016851}
}