  Weakly supervised temporal action localization (WTAL) aims to localize
actions in untrimmed videos with only weak supervision information (e.g.
video-level labels). Most existing models handle all input videos with a fixed
temporal scale. However, such models are not sensitive to actions whose pace of
the movements is different from the ``normal" speed, especially slow-motion
action instances, which complete the movements with a much slower speed than
their counterparts with a normal speed. Here arises the slow-motion blurred
issue: It is hard to explore salient slow-motion information from videos at
``normal" speed. In this paper, we propose a novel framework termed Slow Motion
Enhanced Network (SMEN) to improve the ability of a WTAL network by
compensating its sensitivity on slow-motion action segments. The proposed SMEN
comprises a Mining module and a Localization module. The mining module
generates mask to mine slow-motion-related features by utilizing the
relationships between the normal motion and slow motion; while the localization
module leverages the mined slow-motion features as complementary information to
improve the temporal action localization results. Our proposed framework can be
easily adapted by existing WTAL networks and enable them be more sensitive to
slow-motion actions. Extensive experiments on three benchmarks are conducted,
which demonstrate the high performance of our proposed framework.
