The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the attribute propagation network (APNet), which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.

中文翻译：

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图零样本学习的属性传播网络

零样本学习 (ZSL) 的目标是训练模型对训练期间未见过的类别样本进行分类。为了解决这个具有挑战性的任务，大多数 ZSL 方法通过一组预定义的属性将看不见的测试类与可见的（训练）类联系起来，这些属性可以描述同一语义空间中的所有类，因此在训练类上学到的知识可以适应看不见的课。在本文中，我们的目标是通过训练传播机制来优化 ZSL 的属性空间，以基于类图上的邻居和相关类来细化每个类的语义属性。我们表明，传播的属性可以为零样本类生成分类器，并在不同的 ZSL 设置中显着提高性能。类图通常是免费或非常便宜的，例如 WordNet 或 ImageNet 类。当没有提供图时，给定类的预定义语义嵌入，我们可以学习一种机制来以端到端的方式生成图以及传播机制。然而，这种图形辅助技术在文献中并没有得到很好的探索。在本文中，我们介绍了属性传播网络 (APNet)，它由 1) 为每个类生成属性向量的图传播模型和 2) 参数化最近邻 (NN) 分类器将图像分类到具有最近的类的类图像嵌入的属性向量。为了更好地泛化不可见的类，与以前的方法不同，我们采用元学习策略在多个子图上训练 NN 分类器的传播机制和相似性度量，每个子图都与训练类子集上的分类任务相关联。在两个零样本学习设置和五个基准数据集的实验中，APNet 实现了引人注目的性能或新的最先进的结果。