Continual Semantic Segmentation

Continual semantic segmentation (CSS) focuses on training deep learning models to incrementally learn new object classes for image segmentation without forgetting previously learned ones, a crucial challenge in adapting to dynamic environments. Current research emphasizes mitigating "catastrophic forgetting" through techniques like knowledge distillation, memory replay (often with intelligent sample selection), and architectural innovations such as incorporating lightweight adapters into Vision Transformers (ViTs) or employing low-rank adaptations. This field is significant for advancing robust and adaptable AI systems in applications like autonomous driving, medical image analysis, and robotics, where continuous learning from streaming data is essential.