We strive for spatio-temporal localization of actions in videos. The state-of-the-art relies on action proposals at test time and selects the best one with a classifier demanding carefully annotated box annotations at train time. Annotating action boxes in video is cumbersome, tedious, and error prone. Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only. We introduce an overlap measure between action proposals and points and incorporate them all into the objective of a non-convex Multiple Instance Learning optimization. Experimental evaluation on the UCF Sports and UCF 101 datasets shows that (i) spatio-temporal proposals can be used to train classifiers while retaining the localization performance, (ii) point annotations yield results comparable to box annotations while being significantly faster to annotate, (iii) with a minimum amount of supervision our approach is competitive to the state-of-the-art. Finally, we introduce spatio-temporal action annotations on the train and test videos of Hollywood2, resulting in Hollywood2Tubes, available at tinyurl.com/hollywood2tubes.

该研究旨在通过在视频中对稀疏的一些帧上标注动作的点，利用重叠度量和多实例学习优化的目标，而非繁琐的矩形框注释来提高视频中行动的时空本地化性能，并展示了从Hollywood2训练和测试视频得到的Spatio-temporal行动注释，即《Hollywood2Tubes》。

基于点标注的提议的动作定位