  In computer vision applications, such as domain adaptation (DA), few shot
learning (FSL) and zero-shot learning (ZSL), we encounter new objects and
environments, for which insufficient examples exist to allow for training
"models from scratch," and methods that adapt existing models, trained on the
presented training environment, to the new scenario are required. We propose a
novel visual attribute encoding method that encodes each image as a
low-dimensional probability vector composed of prototypical part-type
probabilities. The prototypes are learnt to be representative of all training
data. At test-time we utilize this encoding as an input to a classifier. At
test-time we freeze the encoder and only learn/adapt the classifier component
to limited annotated labels in FSL; new semantic attributes in ZSL. We conduct
extensive experiments on benchmark datasets. Our method outperforms
state-of-art methods trained for the specific contexts (ZSL, FSL, DA).
