  Zero-shot learning aims to recognize instances of unseen classes, for which
no visual instance is available during training, by learning multimodal
relations between samples from seen classes and corresponding class semantic
representations. These class representations usually consist of either
attributes, which do not scale well to large datasets, or word embeddings,
which lead to poorer performance. A good trade-off could be to employ short
sentences in natural language as class descriptions. We explore different
solutions to use such short descriptions in a ZSL setting and show that while
simple methods cannot achieve very good results with sentences alone, a
combination of usual word embeddings and sentences can significantly outperform
current state-of-the-art.
