  This paper presents a model architecture for encoding the representations of
part-whole hierarchies in images in form of a graph. The idea is to divide the
image into patches of different levels and then treat all of these patches as
nodes for a fully connected graph. A dynamic feature extraction module is used
to extract feature representations from these patches in each graph iteration.
This enables us to learn a rich graph representation of the image that
encompasses the inherent part-whole hierarchical information. Utilizing proper
self-supervised training techniques, such a model can be trained as a general
purpose vision encoder model which can then be used for various vision related
downstream tasks (e.g., Image Classification, Object Detection, Image
Captioning, etc.).
