  A proper semantic representation for encoding side information is key to the
success of zero-shot learning. In this paper, we explore two alternative
semantic representations especially for zero-shot human action recognition:
textual descriptions of human actions and deep features extracted from still
images relevant to human actions. Such side information are accessible on Web
with little cost, which paves a new way in gaining side information for
large-scale zero-shot human action recognition. We investigate different
encoding methods to generate semantic representations for human actions from
such side information. Based on our zero-shot visual recognition method, we
conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic
representations . The results suggest that our proposed text- and image-based
semantic representations outperform traditional attributes and word vectors
considerably for zero-shot human action recognition. In particular, the
image-based semantic representations yield the favourable performance even
though the representation is extracted from a small number of images per class.
