Overlapped 50-50

4 papers with code • 1 benchmarks • 1 datasets

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Datasets


Most implemented papers

PLOP: Learning without Forgetting for Continual Semantic Segmentation

arthurdouillard/CVPR2021_PLOP 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

clovaai/SSUL NeurIPS 2021

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

zhangchbin/rcil CVPR 2022

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

dfki-av/awt-for-ciss 13 Oct 2022

In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift.