Progressive Learning With Cross-Window Consistency for Semi-Supervised Semantic Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Semi-supervised semantic segmentation leverages limited labeled data and abundant unlabeled data for real-world image understanding.
• Current methods struggle to fully exploit the potential of unlabeled images for improved segmentation accuracy.
• Effective utilization of unlabeled data is crucial for advancing semantic segmentation applications.

Purpose of the Study:

• To introduce cross-window consistency (CWC) as a method for extracting auxiliary supervision from unlabeled data.
• To propose a novel CWC-driven progressive learning framework for optimizing deep networks using unlabeled data.
• To enhance the performance of semi-supervised semantic segmentation by mining weak-to-strong constraints.

Main Methods:

• Developed a biased cross-window consistency (BCC) loss function with an importance factor to enforce semantic consistency in overlapping regions.
• Introduced a dynamic pseudo-label memory bank (DPM) to generate high-consistency and high-reliability pseudo-labels.
• Implemented a progressive learning framework that mines weak-to-strong constraints from massive unlabeled data.

Main Results:

• Demonstrated consistent performance gains across diverse datasets, including urban views, medical images, and satellite scenes.
• The proposed CWC-driven framework effectively extracts auxiliary supervision from unlabeled data.
• The BCC loss and DPM significantly contribute to optimizing deep networks for semantic segmentation.

Conclusions:

• The proposed CWC-driven progressive learning framework offers a superior approach to semi-supervised semantic segmentation.
• Effective leveraging of unlabeled data through CWC leads to significant improvements in segmentation accuracy.
• The framework shows broad applicability across various image understanding domains.