  Activity recognition has shown impressive progress in recent years. However,
the challenges of detecting fine-grained activities and understanding how they
are combined into composite activities have been largely overlooked. In this
work we approach both tasks and present a dataset which provides detailed
annotations to address them. The first challenge is to detect fine-grained
activities, which are defined by low inter-class variability and are typically
characterized by fine-grained body motions. We explore how human pose and hands
can help to approach this challenge by comparing two pose-based and two
hand-centric features with state-of-the-art holistic features. To attack the
second challenge, recognizing composite activities, we leverage the fact that
these activities are compositional and that the essential components of the
activities can be obtained from textual descriptions or scripts. We show the
benefits of our hand-centric approach for fine-grained activity classification
and detection. For composite activity recognition we find that decomposition
into attributes allows sharing information across composites and is essential
to attack this hard task. Using script data we can recognize novel composites
without having training data for them.
