论文标题
设定的viterbi，用于设定监督的动作分割
Set-Constrained Viterbi for Set-Supervised Action Segmentation
论文作者
论文摘要
本文是关于弱监督的行动细分，基本真相仅指定了培训视频中存在的一组动作，但不指定其真正的时间顺序。先前的工作通常使用分类器，该分类器独立标记视频帧来生成伪地面真相，并多次实例学习用于培训分类器。我们通过指定HMM来扩展此框架，该框架是行动类及其时间长度的共同发生的，并通过明确训练基于Viterbi的损失的HMM。我们的第一个贡献是制定新的设置受限的Viterbi算法（SCV）。给定视频，SCV生成了满足地面真相的地图动作细分。在我们的HMM培训中，该预测被用作伪造地面真相。我们在培训方面的第二个贡献是分享相同动作类别的培训视频之间的特征亲和力的新正规化。关于早餐，MPII Cooking2，好莱坞扩展数据集的动作细分和对齐的评估，这证明了我们对先前工作的两项任务的显着改进。
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.
