Disjoint 15 1 On Pascal Voc 2012

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
MBS	78.1	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SSUL-M	68.58	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	64.01	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	54.7	Representation Compensation Networks for Continual Semantic Segmentation
SDR	48.7	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	46.5	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	39.9	Modeling the Background for Incremental Learning in Semantic Segmentation
ILT	7.9	Incremental Learning Techniques for Semantic Segmentation
LWF	5.3	Learning without Forgetting

0 of 9 row(s) selected.

Disjoint 15 1 On Pascal Voc 2012

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
MBS	78.1	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SSUL-M	68.58	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	64.01	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	54.7	Representation Compensation Networks for Continual Semantic Segmentation
SDR	48.7	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	46.5	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	39.9	Modeling the Background for Incremental Learning in Semantic Segmentation
ILT	7.9	Incremental Learning Techniques for Semantic Segmentation
LWF	5.3	Learning without Forgetting

0 of 9 row(s) selected.