Multiclass triplet metric-learning network combined with feature mixing block for few shot learning

摘要

Few-shot learning aims to learn classification with only a few training examples per class. The metric-based approaches aim to learn a set of embedding functions, so that when represented in this embedding, images are easy to recognize. For metric-based few-shot learning, how to get the class feature representation under a few support samples and what metric to use are important. We propose a multiclass triplet metric-learning network combined with a simple foreground-background feature mixing block. With the foreground-background feature mixing block, we "hallucinate" the information from few support examples to get conceptual representation of classes, which is effective to promote few-shot learning. Furthermore, using the multiclass triplet loss, it learns a feature embedding function that could bring similar samples close to each other and keep samples of different classes far apart. Our proposed network is trained in an end-to-end manner from scratch, so as to learn a good embedding function, conceptual representation of classes, and a nonlinear metric simultaneously. Experimental results on the challenging datasets show that our method with Conv-64F feature extracting block is competitive and effective compared to the metric-based baselines with Conv-64F. (C) 2021 SPIE and IS&T