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.

本文介绍了一种用于动作分段的弱监视训练方法，通过多层感知器和隐马尔可夫模型生成虚拟标注视频帧，使用锚点约束维特比算法生成伪标注数据，且在比较三个数据集的表现时相对于过去的方法有了更好的表现

基于 Anchor 约束的集合监督动作分割 Viterbi 算法

Anchor-Constrained Viterbi for Set-Supervised Action Segmentation

Recent crowd counting approaches have achieved excellent performance.
However, they are essentially based on fully supervised paradigm and require
large number of annotated samples. Obtaining annotations is an expensive and
labour-intensive process. In this work, we focus on reducing the annotation
efforts by learning to count in the crowd from limited number of labeled
samples while leveraging a large pool of unlabeled data. Specifically, we
propose a Gaussian Process-based iterative learning mechanism that involves
estimation of pseudo-ground truth for the unlabeled data, which is then used as
supervision for training the network. The proposed method is shown to be
effective under the reduced data (semi-supervised) settings for several
datasets like ShanghaiTech, UCF-QNRF, WorldExpo, UCSD, etc. Furthermore, we
demonstrate that the proposed method can be leveraged to enable the network in
learning to count from synthetic dataset while being able to generalize better
to real-world datasets (synthetic-to-real transfer).

通过半监督学习和高斯过程估算建立虚拟基准，实现充分利用未标注样本数据，学习缩减标注数据的技术，在人群计数领域的多个数据集上表现出了优异的性能，且可实现从虚拟数据集到真实数据集（综合 - 真实转移）的迁移。