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Relative Position and Map Networks in Few-shot Learning for Image Classification

Few-shot learning is an important research topic in image classification, which aims to train robust classifiers to categorize images coming from new classes where only a few labeled samples are available. Recently, metric learning based methods have …

Region Comparison Network for Interpretable Few-shot Image Classification

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 …