  Deep convolutional neural networks (DCNNs) are powerful models that yield
impressive results at object classification. However, recent work has shown
that they do not generalize well to partially occluded objects and to mask
attacks. In contrast to DCNNs, compositional models are robust to partial
occlusion, however, they are not as discriminative as deep models. In this
work, we combine DCNNs and compositional object models to retain the best of
both approaches: a discriminative model that is robust to partial occlusion and
mask attacks. Our model is learned in two steps. First, a standard DCNN is
trained for image classification. Subsequently, we cluster the DCNN features
into dictionaries. We show that the dictionary components resemble object part
detectors and learn the spatial distribution of parts for each object class. We
propose mixtures of compositional models to account for large changes in the
spatial activation patterns (e.g. due to changes in the 3D pose of an object).
At runtime, an image is first classified by the DCNN in a feedforward manner.
The prediction uncertainty is used to detect partially occluded objects, which
in turn are classified by the compositional model. Our experimental results
demonstrate that combining compositional models and DCNNs resolves a
fundamental problem of current deep learning approaches to computer vision: The
combined model recognizes occluded objects, even when it has not been exposed
to occluded objects during training, while at the same time maintaining high
discriminative performance for non-occluded objects.
