  Recent self-supervised video representation learning methods have found
significant success by exploring essential properties of videos, e.g. speed,
temporal order, etc. This work exploits an essential yet under-explored
property of videos, the video continuity, to obtain supervision signals for
self-supervised representation learning. Specifically, we formulate three novel
continuity-related pretext tasks, i.e. continuity justification, discontinuity
localization, and missing section approximation, that jointly supervise a
shared backbone for video representation learning. This self-supervision
approach, termed as Continuity Perception Network (CPNet), solves the three
tasks altogether and encourages the backbone network to learn local and
long-ranged motion and context representations. It outperforms prior arts on
multiple downstream tasks, such as action recognition, video retrieval, and
action localization. Additionally, the video continuity can be complementary to
other coarse-grained video properties for representation learning, and
integrating the proposed pretext task to prior arts can yield much performance
gains.
