Continual Semantic Segmentation
12 papers with code • 3 benchmarks • 2 datasets
Continual learning in semantic segmentation.
ScanNet
ADE20K
Most implemented papers
Unsupervised Model Adaptation for Continual Semantic Segmentation
We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain.
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.
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
arthurdouillard/CVPR2021_PLOP
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classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation
MECLabTUDA/ACS
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Deep learning for medical imaging suffers from temporal and privacy-related restrictions on data availability.
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.
SATS: Self-Attention Transfer for Continual Semantic Segmentation
QIU023/SATS_Continual_Semantic_Seg
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Considering that pixels belonging to the same class in each image often share similar visual properties, a class-specific region pooling is applied to provide more efficient relationship information for knowledge transfer.
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection
Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains.
Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point Clouds
To address this issue, we propose a new label-guided knowledge distillation (LGKD) loss, where the old model output is expanded and transplanted (with the guidance of the ground truth label) to form a semantically appropriate class correspondence with the new model output.
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
zbwxp/SegVit
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This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}.
