  Weakly-supervised temporal action localization aims to localize action
instances in untrimmed videos with only video-level supervision. We witness
that different actions record common phases, e.g., the run-up in the HighJump
and LongJump. These different actions are defined as conjoint actions, whose
rest parts are definite phases, e.g., leaping over the bar in a HighJump.
Compared with the common phases, the definite phases are more easily localized
in existing researches. Most of them formulate this task as a Multiple Instance
Learning paradigm, in which the common phases are tended to be confused with
the background, and affect the localization completeness of the conjoint
actions. To tackle this challenge, we propose a Joint of Common and Definite
phases Network (JCDNet) by improving feature discriminability of the conjoint
actions. Specifically, we design a Class-Aware Discriminative module to enhance
the contribution of the common phases in classification by the guidance of the
coarse definite-phase features. Besides, we introduce a temporal attention
module to learn robust action-ness scores via modeling temporal dependencies,
distinguishing the common phases from the background. Extensive experiments on
three datasets (THUMOS14, ActivityNetv1.2, and a conjoint-action subset)
demonstrate that JCDNet achieves competitive performance against the
state-of-the-art methods. Keywords: weakly-supervised learning, temporal action
localization, conjoint action
