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

Source: [HindSight: A Graph-Based Vision Model Architecture For Representing Part-Whole Hierarchies](<https://www.sota2.com/research/paper/68fa69b01e209131cea236ad>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:05:39.232Z

Authors: Muhammad AbdurRafae

Published: 2021-04-08T12:17:54.000Z

## 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.).

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.03722>)

[PDF](<https://arxiv.org/pdf/2104.03722.pdf>)