This paper addresses the problem of spatiotemporal localization of actions in
videos. Compared to leading approaches, which all learn to localize based on
carefully annotated boxes on training video frames, we adhere to a
weakly-supervised solution that only requires a video class label. We introduce
an actor-supervised architecture that exploits the inherent compositionality of
actions in terms of actor transformations, to localize actions. We make two
contributions. First, we propose actor proposals derived from a detector for
human and non-human actors intended for images, which is linked over time by
Siamese similarity matching to account for actor deformations. Second, we
propose an actor-based attention mechanism that enables the localization of the
actions from action class labels and actor proposals and is end-to-end
trainable. Experiments on three human and non-human action datasets show actor
supervision is state-of-the-art for weakly-supervised action localization and
is even competitive to some fully-supervised alternatives.

本文介绍一种视频动作时空本地化的方法，着重于弱监督学习，只需要视频类标签即可。利用演员在动作中的变换特性，引入基于演员提议的算法及注意力机制，对三个行为数据集进行测试，实验结果表明，该方法在弱监督下可以实现与一些强监督学习相当的性能。