  Compositional convolutional networks are generative compositional models of
neural network features, that achieve state of the art results when classifying
partially occluded objects, even when they have not been exposed to occluded
objects during training. In this work, we study the performance of
CompositionalNets at localizing occluders in images. We show that the original
model is not able to localize occluders well. We propose to overcome this
limitation by modeling the feature activations as a mixture of von-Mises-Fisher
distributions, which also allows for an end-to-end training of
CompositionalNets. Our experimental results demonstrate that the proposed
extensions increase the model's performance at localizing occluders as well as
at classifying partially occluded objects.
