Incremental Semantic Segmentation

Incremental semantic segmentation focuses on training deep learning models to continuously learn new object classes for image segmentation without forgetting previously learned ones. Current research emphasizes mitigating "catastrophic forgetting" and "background shift" through techniques like knowledge distillation, contrastive learning, and novel classifier initialization strategies, often employing convolutional neural networks (CNNs) and increasingly, transformer architectures or hybrid models. This field is crucial for developing robust and adaptable computer vision systems capable of handling real-world scenarios with evolving data distributions, impacting applications such as autonomous driving and remote sensing.