  Part models of object categories are essential for challenging recognition
tasks, where differences in categories are subtle and only reflected in
appearances of small parts of the object. We present an approach that is able
to learn part models in a completely unsupervised manner, without part
annotations and even without given bounding boxes during learning. The key idea
is to find constellations of neural activation patterns computed using
convolutional neural networks. In our experiments, we outperform existing
approaches for fine-grained recognition on the CUB200-2011, NA birds, Oxford
PETS, and Oxford Flowers dataset in case no part or bounding box annotations
are available and achieve state-of-the-art performance for the Stanford Dog
dataset. We also show the benefits of neural constellation models as a data
augmentation technique for fine-tuning. Furthermore, our paper unites the areas
of generic and fine-grained classification, since our approach is suitable for
both scenarios. The source code of our method is available online at
http://www.inf-cv.uni-jena.de/part_discovery
