  Weakly-supervised temporal action localization aims to identify and localize
the action instances in the untrimmed videos with only video-level action
labels. When humans watch videos, we can adapt our abstract-level knowledge
about actions in different video scenarios and detect whether some actions are
occurring. In this paper, we mimic how humans do and bring a new perspective
for locating and identifying multiple actions in a video. We propose a network
named VQK-Net with a video-specific query-key attention modeling that learns a
unique query for each action category of each input video. The learned queries
not only contain the actions' knowledge features at the abstract level but also
have the ability to fit this knowledge into the target video scenario, and they
will be used to detect the presence of the corresponding action along the
temporal dimension. To better learn these action category queries, we exploit
not only the features of the current input video but also the correlation
between different videos through a novel video-specific action category query
learner worked with a query similarity loss. Finally, we conduct extensive
experiments on three commonly used datasets (THUMOS14, ActivityNet1.2, and
ActivityNet1.3) and achieve state-of-the-art performance.
