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Mitigating Background Shift in Class-Incremental Semantic Segmentation

2024-07-16 · Gilhan Park, WonJun Moon, SuBeen Lee, Tae-Young Kim 외

Class-Incremental Semantic Segmentation(CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling …

Class Incremental LearningClass-Incremental Semantic SegmentationContinual LearningContinual Semantic Segmentation+15

Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation

2022-10-13 · Dipam Goswami, René Schuster, Joost Van de Weijer, Didier Stricker

In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although recent works focused on these issues, ex…

Class-Incremental Semantic SegmentationContinual LearningOverlapped 100-10Overlapped 100-5+8

SATS: Self-Attention Transfer for Continual Semantic Segmentation

2022-03-15 · Yiqiao Qiu, Yixing Shen, Zhuohao Sun, Yanchong Zheng 외

Continually learning to segment more and more types of image regions is a desired capability for many intelligent systems. However, such continual semantic segmentation suffers from the same catastrophic forgetting issue…

Continual Semantic SegmentationKnowledge DistillationOverlapped 100-10Overlapped 10-1+7