论文标题
WOAD：未修剪视频中弱监督的在线操作检测
WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
论文作者
论文摘要
未修剪视频中的在线操作检测旨在确定发生的动作，这对于实时应用程序非常重要。以前的方法依赖于训练的时间动作边界的繁琐注释，这阻碍了在线操作检测系统的可扩展性。我们提出了WOAD，这是一个弱监督的框架，只能使用视频级标签进行培训。 WOAD包含两个共同训练的模块，即时间提案生成器（TPG）和在线行动识别器（OAR）。在视频级标签的监督下，TPG在脱机和目标上为桨式伪造框架级标签提供了脱机和目标。借助TPG的监督信号，OAR学会了以在线方式进行操作检测。 Thumos'14，ActivationNet1.2和ActivityNet1.3的实验结果表明，我们弱监督的方法在很大程度上优于弱监督的基线，并实现与先前强烈监督的方法相当的性能。除此之外，WOAD在可用时还可以灵活地利用强大的监督。当受到强烈监督时，我们的方法获得了最新的最新结果，从而完成了在线人均操作识别和在线检测动作开始的任务。
Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a weakly supervised framework that can be trained using only video-class labels. WOAD contains two jointly-trained modules, i.e., temporal proposal generator (TPG) and online action recognizer (OAR). Supervised by video-class labels, TPG works offline and targets at accurately mining pseudo frame-level labels for OAR. With the supervisory signals from TPG, OAR learns to conduct action detection in an online fashion. Experimental results on THUMOS'14, ActivityNet1.2 and ActivityNet1.3 show that our weakly-supervised method largely outperforms weakly-supervised baselines and achieves comparable performance to the previous strongly-supervised methods. Beyond that, WOAD is flexible to leverage strong supervision when it is available. When strongly supervised, our method obtains the state-of-the-art results in the tasks of both online per-frame action recognition and online detection of action start.
