  Parsing an image into a hierarchy of objects, parts, and relations is
important and also challenging in many computer vision tasks. This paper
proposes a simple and effective capsule autoencoder to address this issue,
called DPR-CAE. In our approach, the encoder parses the input into a set of
part capsules, including pose, intensity, and dynamic vector. The decoder
introduces a novel dynamic part representation (DPR) by combining the dynamic
vector and a shared template bank. These part representations are then
regulated by corresponding capsules to composite the final output in an
interpretable way. Besides, an extra translation-invariant module is proposed
to avoid directly learning the uncertain scene-part relationship in our
DPR-CAE, which makes the resulting method achieves a promising performance gain
on $rm$-MNIST and $rm$-Fashion-MNIST. % to model the scene-object relationship
DPR-CAE can be easily combined with the existing stacked capsule autoencoder
and experimental results show it significantly improves performance in terms of
unsupervised object classification. Our code is available in the Appendix.
