Few-shot Image Classification Based on Task-Aware Relation Network

GUO Lihua; WANG Guangfei

doi:10.11999/JEIT230162

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 977-985

GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162

Citation:

GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162

GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162

Citation:

GUO Lihua, WANG Guangfei. Few-shot Image Classification Based on Task-Aware Relation Network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 977-985. doi: 10.11999/JEIT230162

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Few-shot Image Classification Based on Task-Aware Relation Network

doi: 10.11999/JEIT230162

GUO Lihua^,,
WANG Guangfei

School of Electronic and Information, South China University of Technology, Guangzhou 510641, China

Funds: Guangdong Basic and Applied Basic Research Foundation (2022A1515011549, 2023A1515011104)

Received Date: 2023-03-16
Rev Recd Date: 2023-08-17

Available Online: 2023-08-21

Publish Date: 2024-03-27

Abstract

Abstract

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.
- Few-Shot Learning (FSL),
- Image classification,
- Metric learning,
- Task-aware,
- Relation Network(RN)

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References(21)

References

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