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

TL;DR

HindSight introduces a graph-based vision model that encodes part-whole hierarchies in images by representing patches as nodes in a dynamic, fully connected graph, enabling versatile downstream vision tasks.

Contribution

The paper proposes a novel graph architecture for encoding hierarchical image structures, integrating dynamic feature extraction for improved representation learning.

Findings

Effective encoding of part-whole hierarchies in images.

Versatile application to multiple vision tasks.

Self-supervised training enhances generalization.

Abstract

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.).