WeatherSeg: Weather-Robust Image Segmentation using Teacher-Student Dual Learning and Classifier-Updating Attention

2026-04-19

AI Summary

This work addresses the challenges of environmental perception under adverse weather conditions and the high cost of pixel-level annotation in autonomous driving by proposing a semi-supervised semantic segmentation framework. The approach integrates a dual teacher-student weight-sharing model (DTSWSM) with a classifier weight-update attention mechanism (CWUAM), enabling adaptive responses to environmental variations through cross-weather knowledge distillation and dynamic weight adjustment. Evaluated across diverse weather conditions—including clear, rainy, overcast, and foggy scenarios—the proposed framework consistently outperforms existing baselines, achieving state-of-the-art performance in both segmentation accuracy and robustness. These results demonstrate a significant improvement in the model’s generalization capability under real-world, weather-varying driving conditions.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

WeatherSeg, an advanced semi-supervised segmentation framework, addresses autonomous driving's environmental perception challenges in adverse weather while reducing annotation costs. This framework integrates a Dual Teacher-Student Weight-Sharing Model (DTSWSM) that enables knowledge distillation from weather-affected images, and a Classifier Weight Updating Attention Mechanism (CWUAM) that dynamically adjusts classifier weights based on environmental attributes. Comprehensive evaluations demonstrate that WeatherSeg significantly outperforms baseline models in both accuracy and robustness across various weather conditions, including clear, rainy, cloudy, and foggy scenarios, establishing it as an effective solution for all-weather semantic segmentation in autonomous driving and related applications.

Problem

• weather-robust segmentation
• autonomous driving
• adverse weather
• semantic segmentation
• annotation cost

Innovation

• Weather-Robust Segmentation
• Teacher-Student Dual Learning
• Classifier-Updating Attention
• Semi-Supervised Learning
• Autonomous Driving Perception