SSUL: Semantic Segmentation with Unknown Label for Exemplar-based
Class-Incremental Learning
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based
Class-Incremental Learning
This paper introduces a solid state-of-the-art baseline for a class-incremental semantic segmentation (CISS) problem. While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophic forgetting; the semantic drift of the …