Visual recognition in real-world requires handling long-tailed and even open-ended data. It is a practical utility of a visual system to reliably recognizing the populated "head" visual concepts and meanwhile to learn about "tail" categories of few instances. Class-balanced many-shot learning and few-shot learning tackle one side of this challenging problem, via either learning strong classifiers for populated categories or few-shot classifiers for the tail classes. In this paper, we investigate the problem of generalized few-shot learning, where recognition on the head and the tail are performed jointly. We propose a neural dictionary-based ClAssifier SynThesis LEarning (CASTLE) approach to synthesizes the calibrated "tail" classifiers in addition to the multi-class "head" classifiers, and simultaneously recognizes the head and tail visual categories in a global discerning framework. CASTLE has demonstrated superior performances across different learning scenarios, i.e., many-shot learning, few-shot learning, and generalized few-shot learning, on two standard benchmark datasets --- MiniImageNet and TieredImageNet.

这篇论文研究了在物体识别中应对长尾或无限制问题的方法，提出了一种名为 CASTLE 和 ACASTLE 的模型，可以同时识别常见类别并学习少量样本的新出现类别，且可以在多领域中应用。这两种模型在 MiniImageNet 和 TieredImageNet 数据集上的表现优于现有的 GFSL 和强基准方法，且实验结果表明在标准的几种情况下，这两种方法的表现均优于以前的 最新方法。

适应性分类器合成学习用于泛化的少样本学习