Poster: Class-Balanced Exemplar Memory Selection for Class-Incremental Semantic Segmentation

Class-incremental semantic segmentation (CISS) is a challenging task for operating image analysis from mobile devices in a changing real-world environment. In CISS, data-replay method is effective which selects and stores a part of past class data as the exemplar memory to learn increasing class objects sequentially without forgetting. We set SSUL-M as our baseline and propose the exemplar memory selection method considering the objects' classes.