Region Comparison Network for Interpretable Few-shot Image Classification

Understand

While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data.

• However, the annotation is often expensive and time-consuming.
• Few-shot image classification has thus been proposed to effectively use only a limited number of labeled examples to train models for new classes.
• Recent works based on transferable metric learning methods have achieved promising classification performance through learning the similarity between the features of samples from the query and support sets.