Disjoint 10 1 On Pascal Voc 2012

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
SSUL-M	53.50	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	50.87	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	18.2	Representation Compensation Networks for Continual Semantic Segmentation
SDR	14.3	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	8.4	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	6.9	Modeling the Background for Incremental Learning in Semantic Segmentation
ILT	5.4	Incremental Learning Techniques for Semantic Segmentation
LWF	4.3	Learning without Forgetting

0 of 8 row(s) selected.

Disjoint 10 1 On Pascal Voc 2012

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
SSUL-M	53.50	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	50.87	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	18.2	Representation Compensation Networks for Continual Semantic Segmentation
SDR	14.3	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	8.4	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	6.9	Modeling the Background for Incremental Learning in Semantic Segmentation
ILT	5.4	Incremental Learning Techniques for Semantic Segmentation
LWF	4.3	Learning without Forgetting

0 of 8 row(s) selected.