Overlapped 15 5 On Pascal Voc 2012

Metrics

Mean IoU (val)

Results

Performance results of various models on this benchmark

		Paper Title
MBS	82.6	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SATS-M	78.72	SATS: Self-Attention Transfer for Continual Semantic Segmentation
SATS	75.70	SATS: Self-Attention Transfer for Continual Semantic Segmentation
SSUL-M	73.02	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	72.4	Representation Compensation Networks for Continual Semantic Segmentation
SSUL+AWT	71.4	Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
SSUL	71.22	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SDR	70.1	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	70.09	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	70.08	PLOP: Learning without Forgetting for Continual Semantic Segmentation
PLOPLong	69.37	Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
ILT	61.3	Incremental Learning Techniques for Semantic Segmentation
LWF	55.0	Learning without Forgetting

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Overlapped 15 5 On Pascal Voc 2012

Metrics

Mean IoU (val)

Results

Performance results of various models on this benchmark

		Paper Title
MBS	82.6	Mitigating Background Shift in Class-Incremental Semantic Segmentation
SATS-M	78.72	SATS: Self-Attention Transfer for Continual Semantic Segmentation
SATS	75.70	SATS: Self-Attention Transfer for Continual Semantic Segmentation
SSUL-M	73.02	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
RCNet-101	72.4	Representation Compensation Networks for Continual Semantic Segmentation
SSUL+AWT	71.4	Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation
SSUL	71.22	SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
SDR	70.1	Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations
PLOP	70.09	PLOP: Learning without Forgetting for Continual Semantic Segmentation
MiB	70.08	PLOP: Learning without Forgetting for Continual Semantic Segmentation
PLOPLong	69.37	Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
ILT	61.3	Incremental Learning Techniques for Semantic Segmentation
LWF	55.0	Learning without Forgetting

0 of 13 row(s) selected.