In this paper we analyse the classification of zoological illustrations. Historically, zoological illustrations were the modus operandi for the documentation of new species, and now serve as crucial sources for long-term ecological and biodiversity research. By employing computational methods for classification, the data can be made amenable to research. Automated species identification is challenging due to the long-tailed nature of the data, and the millions of possible classes in the species taxonomy. Success commonly depends on large training sets with many examples per class, but images from only a subset of classes are digitally available, and many images are unlabelled, since labelling requires domain expertise. We explore zero-shot learning to address the problem, where features are learned from classes with medium to large samples, which are then transferred to recognise classes with few or no training samples. We specifically explore how distributed, multi-modal background knowledge from data providers, such as the Global Biodiversity Information Facility (GBIF), iNaturalist, and the Biodiversity Heritage Library (BHL), can be used to share knowledge between classes for zero-shot learning. We train a prototypical network for zero-shot classification, and introduce fused prototypes (FP) and hierarchical prototype loss (HPL) to optimise the model. Finally, we analyse the performance of the model for use in real-world applications. The experimental results are encouraging, indicating potential for use of such models in an expert support system, but also express the difficulty of our task, showing a necessity for research into computer vision methods that are able to learn from small samples.

在本文中，我们分析了动物学插图的分类。从历史上看，动物学插图是记录新物种的惯用方法，现在已成为长期生态和生物多样性研究的重要来源。通过采用计算方法进行分类，可以使数据易于研究。由于数据的长尾性质以及物种分类学中数百万种可能的类别，因此自动进行物种识别具有挑战性。成功通常取决于大型培训集，每个班级都有很多示例，但是数字化仅提供了一部分班级的图像，并且许多图像都未标记，因为标记需要领域专业知识。我们探索零镜头学习来解决该问题，即从具有中型到大型样本的类中学习特征，然后将其转移到识别样本很少或没有训练样本的班级中。我们专门探讨了如何利用数据提供者（例如全球生物多样性信息设施（GBIF），iNaturalist和生物多样性遗产图书馆（BHL））中的分布式多模式背景知识在各个类别之间共享知识，以实现零镜头学习。我们训练了用于零击分类的原型网络，并引入了融合原型（FP）和分层原型损失（HPL）来优化模型。最后，我们分析了在实际应用中使用的模型的性能。实验结果令人鼓舞，表明在专家支持系统中使用此类模型的潜力，但也表达了我们的工作难度，