Disjoint 15 5 On Pascal Voc 2012

Metrics

Mean IoU

Results

Performance results of various models on this benchmark

		Paper Title
MBS	79.0	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SSUL-M	69.83	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	69.10	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SDR	67.3	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
RCNet-101	67.3	Representation Compensation Networks for Continual Semantic Segmentation
MiB	65.9	Modeling the Background for Incremental Learning in Semantic Segmentation
PLOP	64.3	PLOP: Learning without Forgetting for Continual Semantic Segmentation
ILT	58.9	Incremental Learning Techniques for Semantic Segmentation
LWF	54.9	Learning without Forgetting

0 of 9 row(s) selected.

Disjoint 15 5 On Pascal Voc 2012

Metrics

Mean IoU

Results

Performance results of various models on this benchmark

		Paper Title
MBS	79.0	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SSUL-M	69.83	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SSUL	69.10	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SDR	67.3	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
RCNet-101	67.3	Representation Compensation Networks for Continual Semantic Segmentation
MiB	65.9	Modeling the Background for Incremental Learning in Semantic Segmentation
PLOP	64.3	PLOP: Learning without Forgetting for Continual Semantic Segmentation
ILT	58.9	Incremental Learning Techniques for Semantic Segmentation
LWF	54.9	Learning without Forgetting

0 of 9 row(s) selected.

Disjoint 15 5 On Pascal Voc 2012 | SOTA | HyperAI