Overlapped 100-5
5 papers with code • 1 benchmarks • 1 datasets
ADE20K
Most implemented papers
Incremental Learning Techniques for Semantic Segmentation
LTTM/IL-SemSegm
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To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones.
PLOP: Learning without Forgetting for Continual Semantic Segmentation
arthurdouillard/CVPR2021_PLOP
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CVPR 2021
classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
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 background class and the multi-label prediction issue.
Representation Compensation Networks for Continual Semantic Segmentation
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.
