  We investigate the importance of parts for the tasks of action and attribute
classification. We develop a part-based approach by leveraging convolutional
network features inspired by recent advances in computer vision. Our part
detectors are a deep version of poselets and capture parts of the human body
under a distinct set of poses. For the tasks of action and attribute
classification, we train holistic convolutional neural networks and show that
adding parts leads to top-performing results for both tasks. In addition, we
demonstrate the effectiveness of our approach when we replace an oracle person
detector, as is the default in the current evaluation protocol for both tasks,
with a state-of-the-art person detection system.
