  In this paper, we investigate large-scale zero-shot activity recognition by
modeling the visual and linguistic attributes of action verbs. For example, the
verb "salute" has several properties, such as being a light movement, a social
act, and short in duration. We use these attributes as the internal mapping
between visual and textual representations to reason about a previously unseen
action. In contrast to much prior work that assumes access to gold standard
attributes for zero-shot classes and focuses primarily on object attributes,
our model uniquely learns to infer action attributes from dictionary
definitions and distributed word representations. Experimental results confirm
that action attributes inferred from language can provide a predictive signal
for zero-shot prediction of previously unseen activities.
