Weakly supervised temporal action localization, which aims at temporally
locating action instances in untrimmed videos using only video-level class
labels during training, is an important yet challenging problem in video
analysis. Many current methods adopt the "localization by classification"
framework: first do video classification, then locate temporal area
contributing to the results most. However, this framework fails to locate the
entire action instances and gives little consideration to the local context. In
this paper, we present a novel architecture called Cascaded Pyramid Mining
Network (CPMN) to address these issues using two effective modules. First, to
discover the entire temporal interval of specific action, we design a two-stage
cascaded module with proposed Online Adversarial Erasing (OAE) mechanism, where
new and complementary regions are mined through feeding the erased feature maps
of discovered regions back to the system. Second, to exploit hierarchical
contextual information in videos and reduce missing detections, we design a
pyramid module which produces a scale-invariant attention map through combining
the feature maps from different levels. Final, we aggregate the results of two
modules to perform action localization via locating high score areas in
temporal Class Activation Sequence (CAS). Extensive experiments conducted on
THUMOS14 and ActivityNet-1.3 datasets demonstrate the effectiveness of our
method.

本文介绍了一种名为级联金字塔挖掘网络的新型体系结构，旨在通过两个有效模块解决弱监督的时间动作定位问题，并在 THUMOS14 和 ActivityNet-1.3 数据集上进行了广泛的实验和验证。