Unsupervised Neural Network Part Segmentation Under Occlusion

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Solution Overview

Problem

Existing object analysis models struggle with robustness to variations such as object transformations, deformations, occlusions, and pose variations due to the computational expense and manual annotation requirements of current 2D part representations.

Innovation Solution

A self-supervised deep learning framework for part segmentation using unsupervised neural networks trained with geometric concentration, equivariance, and semantic consistency constraints, enabling robust part segment detection without ground truth annotations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If fully-supervised models are used for part segmentation, then detection accuracy is improved, but computational cost and manual annotation requirements increase significantly

Engineering Contradiction:

Improvepart segmentation accuracyVSAvoidannotation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs self-supervised learning by automatically generating supervision signals from the input images themselves through constraint optimization (geometric concentration, equivariance, semantic consistency), eliminating the need for external manual annotations while achieving robust part segmentation

Inventive Principle:

Principle #25Self-service

2Reliability

If manual annotations are used for training, then model performance is improved, but time consumption and productivity decrease

Engineering Contradiction:

Improvemodel performanceVSAvoidtraining efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The training process is automated through self-supervised learning where the model generates its own training signals by optimizing geometric concentration, equivariance, and semantic consistency constraints, eliminating time-consuming manual annotation while maintaining model performance

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The framework pre-computes supervision signals and part segmentations during training by optimizing constraints on the training set, which then enables fast inference on unseen test images without requiring re-annotation or re-training

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If robust part representations are achieved, then adaptability to variations is improved, but computational expense increases

Engineering Contradiction:

Improverobustness to variationsVSAvoidcomputational cost

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The model achieves robustness to camera pose, occlusion, and appearance variations through self-supervised optimization of geometric and semantic constraints, avoiding the need for expensive fully-supervised training while maintaining adaptability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The learned part segmentation model achieves universal applicability across different object categories and variations by optimizing general geometric concentration and semantic consistency constraints that transfer to unseen test images

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12450748B1Segmentation using an unsupervised neural network training technique

Publication Date: 2025.10.21 NVIDIA CORP

AI summary

Systems and methods to detect one or more segments of one or more objects within one or more images based, at least in part, on a neural network trained in an unsupervised manner to infer the one or more segments. Systems and methods to help train one or more neural networks to detect one or more segments of one or more objects within one or more images in an unsupervised manner.