Robust Semantic Segmentation

Robust semantic segmentation aims to create computer vision models that accurately identify and classify objects in images even under challenging conditions like adverse weather, adversarial attacks, or domain shifts. Current research focuses on improving model robustness through techniques such as feature restoration, attention refinement, and the development of novel architectures like transformers and hierarchical grouping networks, often incorporating multi-source data and meta-learning strategies. These advancements are crucial for reliable deployment in safety-critical applications such as autonomous driving and medical image analysis, where accurate and consistent performance is paramount.