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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Interactive Segmentation On Berkeley

평가 지표

NoC@90

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	NoC@90	Paper Title	Repository
SimpleClick (ViT-H, C+L)	1.75	SimpleClick: Interactive Image Segmentation with Simple Vision Transformers	-
f-BRS-B (ResNet-50)	4.34	f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation	-
UCP-Net	2.70	UCP-Net: Unstructured Contour Points for Instance Segmentation	-
ViT-B+MST+CL	1.50	MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation	-
CM guidance	5.60	Content-Aware Multi-Level Guidance for Interactive Instance Segmentation	-
ICL CFR-1 (ViT-H, C+L)	1.46	CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation	-
SimpleClick (ViT-H, SBD)	2.09	SimpleClick: Interactive Image Segmentation with Simple Vision Transformers	-
RITM (HRNet18, SBD)	3.22	Reviving Iterative Training with Mask Guidance for Interactive Segmentation	-
FocalClick-B3-S2	1.48	FocalClick: Towards Practical Interactive Image Segmentation	-
RITM (HRNet18, C+L)	2.26	Reviving Iterative Training with Mask Guidance for Interactive Segmentation	-
BRS	5.08	Interactive Image Segmentation via Backpropagating Refinement Scheme	-
EdgeFlow	2.4	EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow	-
IA+SA	4.94	Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections	-
IA-FP-Net(HRNet, C+L)	2.12	Cascaded Sparse Feature Propagation Network for Interactive Segmentation	-

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Interactive Segmentation On Berkeley | SOTA | HyperAI초신경