Action recognition and detection in the context of long untrimmed video
sequences has seen an increased attention from the research community. However,
annotation of complex activities is usually time consuming and challenging in
practice. Therefore, recent works started to tackle the problem of unsupervised
learning of sub-actions in complex activities. This paper proposes a novel
approach for unsupervised sub-action learning in complex activities. The
proposed method maps both visual and temporal representations to a latent space
where the sub-actions are learnt discriminatively in an end-to-end fashion. To
this end, we propose to learn sub-actions as latent concepts and a novel
discriminative latent concept learning (DLCL) module aids in learning
sub-actions. The proposed DLCL module lends on the idea of latent concepts to
learn compact representations in the latent embedding space in an unsupervised
way. The result is a set of latent vectors that can be interpreted as cluster
centers in the embedding space. The latent space itself is formed by a joint
visual and temporal embedding capturing the visual similarity and temporal
ordering of the data. Our joint learning with discriminative latent concept
module is novel which eliminates the need for explicit clustering. We validate
our approach on three benchmark datasets and show that the proposed combination
of visual-temporal embedding and discriminative latent concepts allow to learn
robust action representations in an unsupervised setting.

本文提出了一种新的对复杂活动中的子动作进行非监督学习的方法，将视觉和时间表示映射到一个空间中，利用 “判别式潜在概念学习” 模块进行无监督的子动作学习，可以在视觉和时间嵌入空间中学习到稳健的动作表示。

复杂活动中基于无监督判别嵌入的子动作学习

Unsupervised Discriminative Embedding for Sub-Action Learning in Complex  Activities

Understanding the structure of complex activities in untrimmed videos is a
challenging task in the area of action recognition. One problem here is that
this task usually requires a large amount of hand-annotated minute- or even
hour-long video data, but annotating such data is very time consuming and can
not easily be automated or scaled. To address this problem, this paper proposes
an approach for the unsupervised learning of actions in untrimmed video
sequences based on a joint visual-temporal embedding space. To this end, we
combine a visual embedding based on a predictive U-Net architecture with a
temporal continuous function. The resulting representation space allows
detecting relevant action clusters based on their visual as well as their
temporal appearance. The proposed method is evaluated on three standard
benchmark datasets, Breakfast Actions, INRIA YouTube Instructional Videos, and
50 Salads. We show that the proposed approach is able to provide a meaningful
visual and temporal embedding out of the visual cues present in contiguous
video frames and is suitable for the task of unsupervised temporal segmentation
of actions.

本文提出了一种基于视觉和时间嵌入空间的非监督学习方法，在不需要手动注释的情况下，通过连续的视频帧中存在的视觉线索，成功地检测到相关的行动群簇，并且适用于时间分割任务。