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

HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies