  Zero-shot learning aims to recognize unseen objects using their semantic
representations. Most existing works use visual attributes labeled by humans,
not suitable for large-scale applications. In this paper, we revisit the use of
documents as semantic representations. We argue that documents like Wikipedia
pages contain rich visual information, which however can easily be buried by
the vast amount of non-visual sentences. To address this issue, we propose a
semi-automatic mechanism for visual sentence extraction that leverages the
document section headers and the clustering structure of visual sentences. The
extracted visual sentences, after a novel weighting scheme to distinguish
similar classes, essentially form semantic representations like visual
attributes but need much less human effort. On the ImageNet dataset with over
10,000 unseen classes, our representations lead to a 64% relative improvement
against the commonly used ones.
