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Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image

Ares Fisher¹, Rajesh P N Rao¹

¹Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.

|November 13, 2023

Summary

Researchers developed Recursive Neural Programs (RNPs), a novel generative model for computer vision. This model learns part-whole hierarchies in images, mimicking human concept learning and visual representation.

Keywords:

artificial intelligence cognitive science deep learning program synthesis

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Area of Science:

Computer Vision
Machine Learning
Cognitive Science

Background:

Human vision relies on part-whole hierarchies for object and scene representation.
Existing neural networks lack a generative model for hierarchical visual understanding.
Compositionality and recursion are key to human concept learning.

Purpose of the Study:

Introduce Recursive Neural Programs (RNPs) as a generative model for part-whole hierarchy learning.
Enable neural networks to model images hierarchically using sensory-motor programs.
Provide a computational framework for understanding human concept representation.

Main Methods:

Developed RNPs, a neural generative model using probabilistic sensory-motor programs.
Modeled images as hierarchical trees, enabling recursive reuse of primitives.
Implemented a grammar for images within different spatial reference frames.

Main Results:

RNPs successfully learned part-whole hierarchies across diverse image datasets.
Demonstrated rich compositionality and parts-based object explanations.
Showcased the model's ability to represent objects hierarchically.

Conclusions:

RNPs offer a powerful approach to learning hierarchical structures in visual data.
The model provides insights into how the human brain might represent concepts recursively.
Suggests a cognitive framework for understanding hierarchical concept learning.