[AAAI 2023 Oral] Enhancing Semantic Segmentation under Adverse Conditions with VBLC
A technique called VBLC is discussed for domain adaptive semantic segmentation under adverse conditions, enhancing images in different weather with a unified model and physical models.
00:00:00 Adaptive semantic segmentation under adverse conditions is crucial for a trustworthy perception system. This video discusses domain adaptation techniques without relying on paired images.
📝 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.
00:02:11 This video discusses domain adaptive semantic segmentation under adverse conditions using VBLC. It focuses on handling different weather conditions with a unified model and using physical models to enhance images.
🔑 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.
00:04:22 This video discusses a method for domain adaptive semantic segmentation under adverse conditions using a VBTC boost module and slowly concerned learning.
🔑 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.
00:06:35 VBLC for Domain Adaptive Semantic Segmentation under Adverse Conditions can enhance images under different conditions. Logic constraint learning loss helps overcome overfitting.
🔑 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.
00:08:47 This video discusses a method for domain adaptive semantic segmentation under adverse conditions using a logic controller. The method involves enhancing target images and using a student-teacher architecture for training. The training process includes three losses and a logic constraint. The video provides a summary of the training algorithm.
🔑 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.
00:11:00 Experimental results and analysis of VBLC for domain adaptive semantic segmentation under adverse conditions. Superior performance in task 6 and adaptation from seascapes to OEC6 and racist hips. Importance of busy boost module and logic constraint learning confirmed.
🔍 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.
00:13:12 This video discusses a technique called VBLC for domain adaptive semantic segmentation under adverse conditions. It introduces the concept of video Gap and logic constructory to improve prediction quality.
🔍 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.
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