  Action recognition has become a rapidly developing research field within the
last decade. But with the increasing demand for large scale data, the need of
hand annotated data for the training becomes more and more impractical. One way
to avoid frame-based human annotation is the use of action order information to
learn the respective action classes. In this context, we propose a hierarchical
approach to address the problem of weakly supervised learning of human actions
from ordered action labels by structuring recognition in a coarse-to-fine
manner. Given a set of videos and an ordered list of the occurring actions, the
task is to infer start and end frames of the related action classes within the
video and to train the respective action classifiers without any need for hand
labeled frame boundaries. We address this problem by combining a framewise RNN
model with a coarse probabilistic inference. This combination allows for the
temporal alignment of long sequences and thus, for an iterative training of
both elements. While this system alone already generates good results, we show
that the performance can be further improved by approximating the number of
subactions to the characteristics of the different action classes as well as by
the introduction of a regularizing length prior. The proposed system is
evaluated on two benchmark datasets, the Breakfast and the Hollywood extended
dataset, showing a competitive performance on various weak learning tasks such
as temporal action segmentation and action alignment.
