Temporal action segmentation approaches have been very successful recently.
However, annotating videos with frame-wise labels to train such models is very
expensive and time consuming. While weakly supervised methods trained using
only ordered action lists require less annotation effort, the performance is
still worse than fully supervised approaches. In this paper, we propose to use
timestamp supervision for the temporal action segmentation task. Timestamps
require a comparable annotation effort to weakly supervised approaches, and yet
provide a more supervisory signal. To demonstrate the effectiveness of
timestamp supervision, we propose an approach to train a segmentation model
using only timestamps annotations. Our approach uses the model output and the
annotated timestamps to generate frame-wise labels by detecting the action
changes. We further introduce a confidence loss that forces the predicted
probabilities to monotonically decrease as the distance to the timestamps
increases. This ensures that all and not only the most distinctive frames of an
action are learned during training. The evaluation on four datasets shows that
models trained with timestamps annotations achieve comparable performance to
the fully supervised approaches.

本文提出了使用时间戳监督来训练时序动作分割模型，并引入置信损失来使模型的学习更加充分，实验结果表明，使用时间戳监督的模型与完全受监督的方法可以达到类似的性能。