  Standard methods for video recognition use large CNNs designed to capture
spatio-temporal data. However, training these models requires a large amount of
labeled training data, containing a wide variety of actions, scenes, settings
and camera viewpoints. In this paper, we show that current convolutional neural
network models are unable to recognize actions from camera viewpoints not
present in their training data (i.e., unseen view action recognition). To
address this, we develop approaches based on 3D representations and introduce a
new geometric convolutional layer that can learn viewpoint invariant
representations. Further, we introduce a new, challenging dataset for unseen
view recognition and show the approaches ability to learn viewpoint invariant
representations.
