  Parts provide a good intermediate representation of objects that is robust
with respect to the camera, pose and appearance variations. Existing works on
part segmentation is dominated by supervised approaches that rely on large
amounts of manual annotations and can not generalize to unseen object
categories. We propose a self-supervised deep learning approach for part
segmentation, where we devise several loss functions that aids in predicting
part segments that are geometrically concentrated, robust to object variations
and are also semantically consistent across different object instances.
Extensive experiments on different types of image collections demonstrate that
our approach can produce part segments that adhere to object boundaries and
also more semantically consistent across object instances compared to existing
self-supervised techniques.
