The automatic pill recognition system is of great significance in improving
the efficiency of the hospital, helping people with visual impairment, and
avoiding cross-infection. However, most existing pill recognition systems based
on deep learning can merely perform pill classification on the learned pill
categories with sufficient training data. In practice, the expensive cost of
data annotation and the continuously increasing categories of new pills make it
meaningful to develop a few-shot class-incremental pill recognition system. In
this paper, we develop the first few-shot class-incremental pill recognition
system, which adopts decoupled learning strategy of representations and
classifiers. In learning representations, we propose the novel Center-Triplet
loss function, which can promote intra-class compactness and inter-class
separability. In learning classifiers, we propose a specialized pseudo pill
image construction strategy to train the Graph Attention Network to obtain the
adaptation model. Moreover, we construct two new pill image datasets for
few-shot class-incremental learning. The experimental results show that our
framework outperforms the state-of-the-art methods.

本文介绍了一种自动药丸识别系统，提出了一种基于深度学习的 few-shot 类增量药丸识别系统方法，该方法采用表示和分类器的分离学习策略，并提出了新的 Center-Triplet 损失函数和伪药丸图像构造策略，用于训练 Graph Attention Network 以获得自适应模型，并构建了两个新的药丸图像数据集。实验结果表明，我们的框架优于现有的最先进方法。