  We study the impact of using rich and diverse textual descriptions of classes
for zero-shot learning (ZSL) on ImageNet. We create a new dataset ImageNet-Wiki
that matches each ImageNet class to its corresponding Wikipedia article. We
show that merely employing these Wikipedia articles as class descriptions
yields much higher ZSL performance than prior works. Even a simple model using
this type of auxiliary data outperforms state-of-the-art models that rely on
standard features of word embedding encodings of class names. These results
highlight the usefulness and importance of textual descriptions for ZSL, as
well as the relative importance of auxiliary data type compared to algorithmic
progress. Our experimental results also show that standard zero-shot learning
approaches generalize poorly across categories of classes.
