Аннотация:The Shape Boltzmann Machine (SBM)[6] and its multilabel version MSBM [5] have been
recently introduced as deep generative models that capture the variations of an object
shape. While being more flexible MSBM requires datasets with labeled parts of the objects
for training. In the paper we present an algorithm for training MSBM using binary masks of
objects and the seeds which approximately correspond to the locations of objects parts. The
latter can be obtained from part-based detectors in an unsupervised manner.