[AAAI 2023 Oral] Enhancing Semantic Segmentation under Adverse Conditions with VBLC

📝 Domain adaptation is crucial for semantic segmentation under adverse weather conditions.

🌐 Utilizing geometry correspondence for paired images is demanding and requires extra labeling efforts.

📸 Image validity and probability modeling play a significant role in image enhancement for domain adaptation.

🔑 The goal is to perform domain adaptation in adverse weather conditions.

🌦️ Previous methods focused on adapting to specific weather conditions, but this work aims to handle all adverse weather conditions with a unified model.

📸 Physical models and a video post module are used to enhance and process images for better adaptation.

🔑 The video discusses the proposed VBTC module and slow-concerned learning for domain adaptive semantic segmentation under adverse conditions.

🔍 The method aims to address the overconfidence issue in self-training processes and achieve good generalization performance on the target domain.

📊 The video also introduces the use of adaptive coefficients and discusses the impact of weather conditions on image quality.

🔑 The video discusses a method for handling nighttime images in domain adaptive semantic segmentation.

📚 The VBC Boost module is effective in reducing the domain gap, but there is still a risk of overfitting to wrong labels in self-training.

🔬 To address the overfitting issue, a logic constraint learning loss is proposed.

🔑 Logic control learning can reduce the problem of incompetence.

🧠 The framework involves enhancing target images, using student-teacher architecture, and applying logic constraint learning.

📝 The training process is guided by three losses and the model is trained on both target and source images.

🔍 Experimental results and analysis comparing vblc with state-of-the-art domain adaptation semantic segmentation measures.

📊 Vblc consistently provides good segmentation results in various scenarios, showcasing its versatility.

🧪 Emulation study confirms the importance of the proposed components in improving overall performance.

🔍 The model found by our logic constructory loss had more potential to achieve better prediction quality.

📷 The prediction of our VBLC is much cleaner and more accurate than the baselines.

💡 The VBLC approach includes adjustments to both ends of the network, resulting in better adaptation and handling of counters issue.