  This paper attacks the challenging problem of zero-example video retrieval.
In such a retrieval paradigm, an end user searches for unlabeled videos by
ad-hoc queries described in natural language text with no visual example
provided. Given videos as sequences of frames and queries as sequences of
words, an effective sequence-to-sequence cross-modal matching is required. The
majority of existing methods are concept based, extracting relevant concepts
from queries and videos and accordingly establishing associations between the
two modalities. In contrast, this paper takes a concept-free approach,
proposing a dual deep encoding network that encodes videos and queries into
powerful dense representations of their own. Dual encoding is conceptually
simple, practically effective and end-to-end. As experiments on three
benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the
proposed solution establishes a new state-of-the-art for zero-example video
retrieval.
