We present a semi-supervised learning approach to the temporal action
segmentation task. The goal of the task is to temporally detect and segment
actions in long, untrimmed procedural videos, where only a small set of videos
are densely labelled, and a large collection of videos are unlabelled. To this
end, we propose two novel loss functions for the unlabelled data: an action
affinity loss and an action continuity loss. The action affinity loss guides
the unlabelled samples learning by imposing the action priors induced from the
labelled set. Action continuity loss enforces the temporal continuity of
actions, which also provides frame-wise classification supervision. In
addition, we propose an Adaptive Boundary Smoothing (ABS) approach to build
coarser action boundaries for more robust and reliable learning. The proposed
loss functions and ABS were evaluated on three benchmarks. Results show that
they significantly improved action segmentation performance with a low amount
(5% and 10%) of labelled data and achieved comparable results to full
supervision with 50% labelled data. Furthermore, ABS succeeded in boosting
performance when integrated into fully-supervised learning.

提出半监督学习方法解决在只有一小部分有标签数据和大量没有标签数据的工程视频中，发现行动并将其分段的任务，其中两个新的损失函数和自适应边界平滑方法也被提出，并在三个基准测试中评估，结果表明它们显著提高了行为分割性能，并在用少量标记数据（5％和 10％）的情况下取得了与全监督相当的结果。