  Recently, graph neural networks (GNNs) have shown powerful ability to handle
few-shot classification problem, which aims at classifying unseen samples when
trained with limited labeled samples per class. GNN-based few-shot learning
architectures mostly replace traditional metric with a learnable GNN. In the
GNN, the nodes are set as the samples embedding, and the relationship between
two connected nodes can be obtained by a network, the input of which is the
difference of their embedding features. We consider this method of measuring
relation of samples only models the sample-to-sample relation, while neglects
the specificity of different tasks. That is, this method of measuring relation
does not take the task-level information into account. To this end, we propose
a new relation measure method, namely the attention-based task-level relation
module (ATRM), to explicitly model the task-level relation of one sample to all
the others. The proposed module captures the relation representations between
nodes by considering the sample-to-task instead of sample-to-sample embedding
features. We conducted extensive experiments on four benchmark datasets:
mini-ImageNet, tiered-ImageNet, CUB-200-2011, and CIFAR-FS. Experimental
results demonstrate that the proposed module is effective for GNN-based
few-shot learning.
