Incremental Segmentation

Incremental segmentation focuses on training deep learning models to progressively learn new object classes for image segmentation without forgetting previously learned ones, a crucial challenge in open-world scenarios. Current research emphasizes strategies to improve model plasticity and prevent catastrophic forgetting, exploring techniques like generative replay, contrastive learning, and the use of hyperbolic spaces for embedding class relationships. These advancements are significant for applications requiring continuous model adaptation, such as autonomous driving and medical image analysis, where data arrives sequentially and retraining from scratch is impractical. The development of robust and efficient incremental segmentation methods is driving progress in various fields by enabling the creation of more adaptable and versatile AI systems.