This paper is about weakly supervised action segmentation, where the ground
truth specifies only a set of actions present in a training video, but not
their true temporal ordering. Prior work typically uses a classifier that
independently labels video frames for generating the pseudo ground truth, and
multiple instance learning for training the classifier. We extend this
framework by specifying an HMM, which accounts for co-occurrences of action
classes and their temporal lengths, and by explicitly training the HMM on a
Viterbi-based loss. Our first contribution is the formulation of a new
set-constrained Viterbi algorithm (SCV). Given a video, the SCV generates the
MAP action segmentation that satisfies the ground truth. This prediction is
used as a framewise pseudo ground truth in our HMM training. Our second
contribution in training is a new regularization of feature affinities between
training videos that share the same action classes. Evaluation on action
segmentation and alignment on the Breakfast, MPII Cooking2, Hollywood Extended
datasets demonstrates our significant performance improvement for the two tasks
over prior work.

该论文介绍了一种新的基于 HMM 的弱监督行为分割框架，其中提出了一种新的 Viterbi 算法和特征 affinities 的正则化方法来提高算法性能。

基于集合约束的 Viterbi 算法进行基于集合监督的动作分割

Set-Constrained Viterbi for Set-Supervised Action Segmentation

Video learning is an important task in computer vision and has experienced
increasing interest over the recent years. Since even a small amount of videos
easily comprises several million frames, methods that do not rely on a
frame-level annotation are of special importance. In this work, we propose a
novel learning algorithm with a Viterbi-based loss that allows for online and
incremental learning of weakly annotated video data. We moreover show that
explicit context and length modeling leads to huge improvements in video
segmentation and labeling tasks andinclude these models into our framework. On
several action segmentation benchmarks, we obtain an improvement of up to 10%
compared to current state-of-the-art methods.

本文提出了一种基于 Viterbi 损失的学习算法，能够在线和逐步地学习弱标注的视频数据，并展示了显式的上下文和长度建模对于视频分割和标记任务的巨大改进，并将这些模型纳入我们的框架之中，在几个动作分割基准测试中，与当前最先进的方法相比，我们实现了高达 10% 的改进。