Chen, C., Li, K., Wei, W., Zhou, J. T., & Zeng, Z. (2022). Hierarchical Graph Neural Networks for Few-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology, 32(1), 240–252. https://doi.org/10.1109/tcsvt.2021.3058098
Abstract:
Abstract—Recent graph neural network (GNN) based methods
for few-shot learning (FSL) represent the samples of interest
as a fully-connected graph and conduct reasoning on the nodes
flatly, which ignores the hierarchical correlations among nodes.
However, real-world categories may have hierarchical structures,
and for FSL, it is important to extract the distinguishing features
of the categories from individual samples. To explore this, we propose a novel hierarchical graph neural network (HGNN) for FSL,
which consists of three parts, i.e., bottom-up reasoning, top-down
reasoning, and skip connections, to enable the efficient learning of
multi-level relationships. For the bottom-up reasoning, we design
intra-class k-nearest neighbor pooling (intra-class knnPool) and
inter-class knnPool layers, to conduct hierarchical learning for
both the intra- and inter-class nodes. For the top-down reasoning,
we propose to utilize graph unpooling (gUnpool) layers to restore
the down-sampled graph into its original size. Skip connections
are proposed to fuse multi-level features for the final node
classification. The parameters of HGNN are learned by episodic
training with the signal of node losses, which aims to train
a well-generalizable model for recognizing unseen classes with
few labeled data. Experimental results on benchmark datasets
have demonstrated that HGNN outperforms other state-of-theart GNN based methods significantly, for both transductive and
non-transductive FSL tasks. The dataset as well as the source
code can be downloaded online
License type:
Publisher Copyright
Funding Info:
This work was partially funded by the National Key Research and Development Program of China under Grant 2019YFB2103004, in part by the National Natural Science Foundation of China under Grant 61902120, in part by the National Outstanding Youth Science Program of National Natural Science Foundation of China under Grant 61625202, in part by the International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China under Grant 61860206011, in part by the Postdoctoral Science
Foundation of China under Grant 2019M662768 and Grant
2019TQ0086, and in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ5083