Temporal action localization is a challenging computer vision problem with
numerous real-world applications. Most existing methods require laborious
frame-level supervision to train action localization models. In this work, we
propose a framework, called 3C-Net, which only requires video-level supervision
(weak supervision) in the form of action category labels and the corresponding
count. We introduce a novel formulation to learn discriminative action features
with enhanced localization capabilities. Our joint formulation has three terms:
a classification term to ensure the separability of learned action features, an
adapted multi-label center loss term to enhance the action feature
discriminability and a counting loss term to delineate adjacent action
sequences, leading to improved localization. Comprehensive experiments are
performed on two challenging benchmarks: THUMOS14 and ActivityNet 1.2. Our
approach sets a new state-of-the-art for weakly-supervised temporal action
localization on both datasets. On the THUMOS14 dataset, the proposed method
achieves an absolute gain of 4.6% in terms of mean average precision (mAP),
compared to the state-of-the-art. Source code is available at
this https URL

提出一种名为 3C-Net 的框架，通过仅使用动作类别标签及其对应数量的视频级监督来学习具有增强本地化能力的判别性动作特征。通过多项联合项来提高动作特征的区分度，以及通过计数损失项来描绘相邻动作序列，从而达到改进本地化的目的，在两个具有挑战性的基准测试中都取得了新的最优效果，代码可在给定链接中找到。