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A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation.

Rihuan Ke, Angelica I Aviles-Rivero, Saurabh Pandey

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 3, 2022

Summary

This summary is machine-generated.

This study introduces a self-training framework for semi-supervised semantic segmentation, reducing the need for extensive pixel-level annotations. The method effectively generates and refines pseudo-masks from unlabeled data to achieve state-of-the-art performance.

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Supervised semantic segmentation models achieve high performance but require costly pixel-level annotations.
Acquiring large, high-quality segmentation masks for training is a significant bottleneck.

Purpose of the Study:

To develop a semi-supervised semantic segmentation framework that minimizes the need for manual annotations.
To propose a self-training approach for efficient semantic segmentation using unlabeled data.

Main Methods:

A three-stage self-training framework is proposed for semi-supervised semantic segmentation.
The method involves initial supervised training, pseudo-mask generation, and iterative refinement using a multi-task model for consistency.
Higher quality pseudo-masks are utilized in the final training stage.

Main Results:

The proposed framework demonstrates state-of-the-art performance compared to existing semi-supervised methods.
Extensive experiments validate the effectiveness of the self-training approach.
The technique successfully extracts and refines pseudo-mask information from unlabeled data.

Conclusions:

The developed self-training framework offers a viable solution to the annotation cost problem in semantic segmentation.
This approach significantly advances semi-supervised learning for pixel-level annotation tasks.
The method achieves superior results by enforcing segmentation consistency and leveraging unlabeled data effectively.