Action recognition is so far mainly focusing on the problem of classification
of hand selected preclipped actions and reaching impressive results in this
field. But with the performance even ceiling on current datasets, it also
appears that the next steps in the field will have to go beyond this fully
supervised classification. One way to overcome those problems is to move
towards less restricted scenarios. In this context we present a large-scale
real-world dataset designed to evaluate learning techniques for human action
recognition beyond hand-crafted datasets. To this end we put the process of
collecting data on its feet again and start with the annotation of a test set
of 250 cooking videos. The training data is then gathered by searching for the
respective annotated classes within the subtitles of freely available videos.
The uniqueness of the dataset is attributed to the fact that the whole process
of collecting the data and training does not involve any human intervention. To
address the problem of semantic inconsistencies that arise with this kind of
training data, we further propose a semantical hierarchical structure for the
mined classes.

介绍了一个大规模的现实世界数据集，该数据集用于评估人类行为识别的学习技术，进一步提出了在无人干预状态下收集数据和训练的方法，并针对该训练数据出现的语义不一致性问题，提出了一个语义分层结构。