Few-shot Image Classification Based on Task-Aware Relation Network
Considering that Relation Network (RN) ignores the global task correlation information, a Few-Shot Learning(FSL)method based on a Task-Aware Relation Network (TARN) for fully using global task correlation information is proposed in this paper. Method class prototype based on global task relationship is created using the Fuzzy C-Mean (FCM) clustering algorithm, and a Task Correlation Attention mechanism (TCA) is designed to improve the one-vs-one evaluation metric in RN for fusing the global task relationship into features. Compared with RN, in the Mini-ImageNet dataset, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 8.15％ and 7.0％ respectively. While in the Tiered-ImageNet dataset, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 7.81 and 6.7％ respectively. Compared with the position-awareness relation network, in Mini-ImageNet, the classification accuracy of 5-way 1-shot settings is still increased by 1.24％. Compared with other few-shot image classification methods, TARN also achieves the best performance in these two datasets. The combination of the relation network and task correlation can effectively improve the few-shot image classification accuracy.
万方数据
维普
