  Recent findings show that deep convolutional neural networks (DCNNs) do not
generalize well under partial occlusion. Inspired by the success of
compositional models at classifying partially occluded objects, we propose to
integrate compositional models and DCNNs into a unified deep model with innate
robustness to partial occlusion. We term this architecture Compositional
Convolutional Neural Network. In particular, we propose to replace the fully
connected classification head of a DCNN with a differentiable compositional
model. The generative nature of the compositional model enables it to localize
occluders and subsequently focus on the non-occluded parts of the object. We
conduct classification experiments on artificially occluded images as well as
real images of partially occluded objects from the MS-COCO dataset. The results
show that DCNNs do not classify occluded objects robustly, even when trained
with data that is strongly augmented with partial occlusions. Our proposed
model outperforms standard DCNNs by a large margin at classifying partially
occluded objects, even when it has not been exposed to occluded objects during
training. Additional experiments demonstrate that CompositionalNets can also
localize the occluders accurately, despite being trained with class labels
only. The code used in this work is publicly available.
