Class-Incremental Semantic Segmentation

Class-Incremental Semantic Segmentation is a subtask in the field of computer vision that focuses on the problem of incrementally adding new categories to existing ones in semantic segmentation. Its goal is to efficiently learn new categories without forgetting old knowledge, thereby achieving accurate pixel-level classification of images. The application value of this task lies in its ability to dynamically expand the model's recognition capabilities, adapt to the ever-changing real-world environment, and enhance the flexibility and practicality of the system.