Class Incremental Learning

Class Incremental Learning is a computer vision technique that involves progressively learning new categories while maintaining knowledge of old categories in the absence of task identifiers. Its goal is to efficiently adapt to new categories without forgetting previously learned content, thereby enhancing the model's generalization and continuous learning capabilities. This method has significant application value in dynamic environments, effectively addressing issues of data distribution changes and category expansion, making it suitable for visual recognition systems that require real-time updates.