  Temporal action segmentation classifies the action of each frame in (long)
video sequences. Due to the high cost of frame-wise labeling, we propose the
first semi-supervised method for temporal action segmentation. Our method
hinges on unsupervised representation learning, which, for temporal action
segmentation, poses unique challenges. Actions in untrimmed videos vary in
length and have unknown labels and start/end times. Ordering of actions across
videos may also vary. We propose a novel way to learn frame-wise
representations from temporal convolutional networks (TCNs) by clustering input
features with added time-proximity condition and multi-resolution similarity.
By merging representation learning with conventional supervised learning, we
develop an "Iterative-Contrast-Classify (ICC)" semi-supervised learning scheme.
With more labelled data, ICC progressively improves in performance; ICC
semi-supervised learning, with 40% labelled videos, performs similar to
fully-supervised counterparts. Our ICC improves MoF by {+1.8, +5.6, +2.5}% on
Breakfast, 50Salads and GTEA respectively for 100% labelled videos.
