This paper attacks the challenging problem of video retrieval by text. In
such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc
queries described exclusively in the form of a natural-language sentence, 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
crucial. To that end, the two modalities need to be first encoded into
real-valued vectors and then projected into a common space. In this paper we
achieve this by proposing a dual deep encoding network that encodes videos and
queries into powerful dense representations of their own. Our novelty is
two-fold. First, different from prior art that resorts to a specific
single-level encoder, the proposed network performs multi-level encoding that
represents the rich content of both modalities in a coarse-to-fine fashion.
Second, different from a conventional common space learning algorithm which is
either concept based or latent space based, we introduce hybrid space learning
which combines the high performance of the latent space and the good
interpretability of the concept space. Dual encoding is conceptually simple,
practically effective and end-to-end trained with hybrid space learning.
Extensive experiments on four challenging video datasets show the viability of
the new method.

本文提出了一种新的视频检索方法，采用双重深度编码网络进行多级编码，将视频和查询作为两种模态编码为向量，同时结合好的可解释性和性能的高性能的概念空间和潜在空间来进行深空间学习，经实验证明了方法的可行性。

文本视频双编码检索

Dual Encoding for Video Retrieval by Text

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.

这篇论文提出了一种新的方案，采用双重深度编码网络，将视频和查询编码成强大的密集表示，并在零样本视频检索的三个基准测试中实现了最新的成果。