  This paper is about action segmentation under weak supervision in training,
where the ground truth provides only a set of actions present, but neither
their temporal ordering nor when they occur in a training video. We use a
Hidden Markov Model (HMM) grounded on a multilayer perceptron (MLP) to label
video frames, and thus generate a pseudo-ground truth for the subsequent
pseudo-supervised training. In testing, a Monte Carlo sampling of action sets
seen in training is used to generate candidate temporal sequences of actions,
and select the maximum posterior sequence. Our key contribution is a new
anchor-constrained Viterbi algorithm (ACV) for generating the pseudo-ground
truth, where anchors are salient action parts estimated for each action from a
given ground-truth set. Our evaluation on the tasks of action segmentation and
alignment on the benchmark Breakfast, MPII Cooking2, Hollywood Extended
datasets demonstrates our superior performance relative to that of prior work.
