  This paper tackles a new problem in computer vision: mid-stream
video-to-video retrieval. This task, which consists in searching a database for
content similar to a video right as it is playing, e.g. from a live stream,
exhibits challenging characteristics. Only the beginning part of the video is
available as query and new frames are constantly added as the video plays out.
To perform retrieval in this demanding situation, we propose an approach based
on a binary encoder that is both predictive and incremental in order to (1)
account for the missing video content at query time and (2) keep up with
repeated, continuously evolving queries throughout the streaming. In
particular, we present the first hashing framework that infers the unseen
future content of a currently playing video. Experiments on FCVID and
ActivityNet demonstrate the feasibility of this task. Our approach also yields
a significant mAP@20 performance increase compared to a baseline adapted from
the literature for this task, for instance 7.4% (2.6%) increase at 20% (50%) of
elapsed runtime on FCVID using bitcodes of size 192 bits.
