We are given a set of video clips, each one annotated with an {\em ordered}
list of actions, such as "walk" then "sit" then "answer phone" extracted from,
for example, the associated text script. We seek to temporally localize the
individual actions in each clip as well as to learn a discriminative classifier
for each action. We formulate the problem as a weakly supervised temporal
assignment with ordering constraints. Each video clip is divided into small
time intervals and each time interval of each video clip is assigned one action
label, while respecting the order in which the action labels appear in the
given annotations. We show that the action label assignment can be determined
together with learning a classifier for each action in a discriminative manner.
We evaluate the proposed model on a new and challenging dataset of 937 video
clips with a total of 787720 frames containing sequences of 16 different
actions from 69 Hollywood movies.

本文介绍了一种基于弱监督时序标注的视频动作分类方法，将视频分为多个时间间隔并为每个时间间隔分配一个动作标签，以实现动作时序的定位，并学习每个动作的鉴别器。通过新的数据集对该方法进行了测试。