Few-shot learning with saliency maps as additional visual information

Abstract

Few-shot learning aims to learn to recognize new object categories from few training examples. Recently, few-shot learning methods have made significant progress. However, most of these methods are based on the concept of learning relations between only the image features in order to recognize objects and this alone may not be sufficient due to the training data scarcity. Therefore, this study focuses on providing saliency maps as additional visual information that describes the shape of the objects and supports few-shot visual learning. In this paper, we propose a simple few-shot learning method called Few-shot Learning with Saliency Maps as Additional Visual Information (SMAVI). Our method encodes the images and the saliency maps, then it learns the deep relations between the combined image features and saliency map features of the objects, where the saliency maps are extracted from the images using a saliency network. The experimental results show that the proposed method outperforms the related state of the art methods on standard few-shot learning datasets.