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Interactive Segmentation On Grabcut

Metrics

NoC@85

NoC@90

Results

Performance results of various models on this benchmark

			Paper Title	Repository
DOS w/o GC	8.02	12.59	Deep Interactive Object Selection
DOS with GC	5.08	6.08	Deep Interactive Object Selection
RIS-Net	-	5.00	Regional Interactive Image Segmentation Networks	-
Latent diversity	3.20	4.79	Interactive Image Segmentation With Latent Diversity	-
BRS	2.60	3.60	Interactive Image Segmentation via Backpropagating Refinement Scheme	-
CM guidance	-	3.58	Content-Aware Multi-Level Guidance for Interactive Instance Segmentation	-
IA+SA	-	3.07	Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections	-
UCP-Net	2	2.76	UCP-Net: Unstructured Contour Points for Instance Segmentation	-
f-BRS-B (ResNet-34)	2	2.46	f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
RITM (HRNet18, SBD)	1.76	2.04	Reviving Iterative Training with Mask Guidance for Interactive Segmentation
EdgeFlow	1.6	1.72	EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow
IA-FP-Net	-	1.68	Cascaded Sparse Feature Propagation Network for Interactive Segmentation
RITM (HRNet18, C+L)	1.42	1.54	Reviving Iterative Training with Mask Guidance for Interactive Segmentation
ViT-B+MST+CL	-	1.48	MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation
SimpleClick (ViT-H, SBD)	1.32	1.44	SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
ICL CFR-1 (ViT-H, SBD)	-	1.42	CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation
SimpleClick (ViT-L, C+L)	1.32	1.40	SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
SimpleClick CFR-1 (ViT-H, SBD)	1.30	1.32	CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

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Interactive Segmentation On Grabcut | SOTA | HyperAI