Bayesian Pixel Selection for Sparse Semantic Segmentation Labeling
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Solution Overview
Problem
Semantic segmentation models require costly and challenging data preparation due to the need for per-pixel annotation, which is time-consuming and difficult to scale.
Innovation Solution
A Bayesian Active Learning (AL) model, named PixelBABA, uses a pixel-wise acquisition function based on Beta Approximation (BAP) to select the most informative samples for labeling, reducing annotation cost by utilizing sparse pixel annotation and capturing both aleatoric and epistemic uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully supervised training with per-pixel annotation is used, then model accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The patent segments the annotation task from the image level to the pixel level, allowing selective annotation of only certain pixels rather than requiring all pixels to be annotated. This enables the model to learn from sparse pixel-level annotations while maintaining high accuracy, directly resolving the contradiction between full annotation requirements and time efficiency
Solution Approach 2:
The model performs self-training by leveraging the sparse annotated pixels to learn and generalize to unannotated regions. The Bayesian active learning framework enables the model to identify informative pixels and use them to improve its own predictions across the entire image, reducing dependency on extensive manual annotation
2Measurement precision
If more pixels are annotated, then model performance is improved, but annotation cost increases
Solution Approach 1:
The patent changes the parameter of annotation density from dense (all pixels) to sparse (selective pixels). By using Bayesian active learning to identify the most informative pixels based on uncertainty metrics, the system achieves high performance with a small fraction of labeled pixels, directly addressing the contradiction between performance and annotation quantity
Solution Approach 2:
The Bayesian active learning framework acts as an intermediary that selects which pixels to annotate based on their informativeness. Instead of annotating all pixels or using random sampling, the intermediary mechanism identifies and prioritizes only the most valuable pixels for annotation, reducing the total number of labeled pixels needed while maintaining performance
3Productivity
If uncertainty-based subset selection is used, then annotation efficiency is improved, but reliable uncertainty measurement is difficult to achieve
Solution Approach 1:
The patent replaces standard deep learning uncertainty estimation with Bayesian deep learning methods. By using Bayesian neural networks with Monte Carlo dropout, the system obtains reliable uncertainty measurements that properly account for both aleatoric and epistemic uncertainty, enabling trustworthy active learning without requiring custom model architectures or loss functions
Data Source
AI summary
Training of a machine vision model, a segmentation model, is performed by using an acquisition function for a small number of pixels of one or more training images. The acquisition function uses first mutual information and second mutual information to identify unlabelled pixels which are labelled with high uncertainty when predicting possible label values. Training, prediction of labels, identifying pixels with highly uncertain labels, obtaining labels only for those pixels with highly uncertain labels and retraining are performed iteratively to finally provide the machine vision model. The iterative approach uses very few labelled pixels to obtain the final machine vision model. The machine vision model accurately labels areas of a data image.