Due to the importance of zero-shot learning, i.e., classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g., pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.

中文翻译：

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零射击学习—对好，坏和丑的综合评价

由于零击学习的重要性，即在缺少标记训练数据的情况下对图像进行分类，因此，最近提出的方法数量稳步增长。我们认为现在应该退后一步，分析该地区的现状。本文的目的是三方面的。首先，鉴于没有达成共识的零击学习基准，我们首先通过统一评估协议和用于此任务的公开可用数据集的数据拆分来定义新基准。这是一个重要的贡献，因为公布的结果通常不可比，有时甚至由于（例如）零击测试类的预训练而有缺陷。此外，我们提出了一个新的零击学习数据集，“动物属性2（AWA2）”数据集，我们在图像特征和图像本身方面都公开可用。其次，我们在深度上比较和分析了大量最新技术，既包括传统的零镜头设置，也包括更现实的广义零镜头设置。最后，我们详细讨论了该地区当前状况的局限性，可以将其作为推进该地区发展的基础。