We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach avoids the laborious attribute-class relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets.

本论文提出了一种基于名称的无监督零样本学习方法，通过区别性地学习单词表示，使得类别和属性名称的相似性与视觉相似性相符，突破了传统零样本学习方法依赖属性特征标注的限制，并且能够在纯文本数据上进行训练，实验结果表明该方法在三个基准数据集上取得了最先进的性能。

Attributes2Classname：一种基于属性的判别式无监督零样本学习模型