  We study the problem of unsupervised discovery and segmentation of object
parts, which, as an intermediate local representation, are capable of finding
intrinsic object structure and providing more explainable recognition results.
Recent unsupervised methods have greatly relaxed the dependency on annotated
data which are costly to obtain, but still rely on additional information such
as object segmentation mask or saliency map. To remove such a dependency and
further improve the part segmentation performance, we develop a novel approach
by disentangling the appearance and shape representations of object parts
followed with reconstruction losses without using additional object mask
information. To avoid degenerated solutions, a bottleneck block is designed to
squeeze and expand the appearance representation, leading to a more effective
disentanglement between geometry and appearance. Combined with a
self-supervised part classification loss and an improved geometry concentration
constraint, we can segment more consistent parts with semantic meanings.
Comprehensive experiments on a wide variety of objects such as face, bird, and
PASCAL VOC objects demonstrate the effectiveness of the proposed method.
