Learning Style Correlation For Elaborate Few-Shot Classification

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Few-shot classification is defined as a task where the network aims to classify unseen classes given only a few samples. Recent approaches, especially metric-based methods, have great progress in few-shot classification. However, the existing metric-based methods have a limitation in deploying discriminative features for elaborate comparison. They usually extract features from the embedding network without direct consideration of the relationship between support and query sets. To address the relationship, we propose a novel architecture, Style Correlated Module (SCM) to learn style correlation between support and query sets for few-shot classification. The proposed module leads support and query feature maps to focus on significant style correlated features and encourage the metric network to conduct an elaborate comparison. Furthermore, the proposed module can be generally applied to the existing metric-based approaches by adding the SCM behind the embedding network. We evaluate our proposed method with comprehensive experiments on two publicly available datasets and demonstrate its effectiveness with comparable results.
Publisher
IEEE Signal Processing Society
Issue Date
2020-10-25
Language
English
Citation

IEEE International Conference on Image Processing (ICIP) 2020, pp.1791 - 1795

ISSN
1522-4880
DOI
10.1109/ICIP40778.2020.9190685
URI
http://hdl.handle.net/10203/274932
Appears in Collection
EE-Conference Papers(학술회의논문)
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