Log In Sign Up

ATRM: Attention-based Task-level Relation Module for GNN-based Few-shot Learning

by   Yurong Guo, et al.

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.


Attentive Graph Neural Networks for Few-Shot Learning

Graph Neural Networks (GNN) has demonstrated the superior performance in...

Hybrid Graph Neural Networks for Few-Shot Learning

Graph neural networks (GNNs) have been used to tackle the few-shot learn...

High-order structure preserving graph neural network for few-shot learning

Few-shot learning can find the latent structure information between the ...

Memory-Augmented Relation Network for Few-Shot Learning

Metric-based few-shot learning methods concentrate on learning transfera...

Deep Comparison: Relation Columns for Few-Shot Learning

Few-shot deep learning is a topical challenge area for scaling visual re...

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem. One nota...

S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization

Camera relocalization is the key component of simultaneous localization ...