SOTAVerified

Segmentation

Papers

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Showing 6001–6025 of 13072 papers

TitleStatusHype
Incomplete Utterance Rewriting as Semantic Segmentation—0
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds—0
K-SAM: A Prompting Method Using Pretrained U-Net to Improve Zero Shot Performance of SAM on Lung Segmentation in CXR Images—0
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model—0
In-context learning for medical image segmentation—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation—0
Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos—0
CMaP-SAM: Contraction Mapping Prior for SAM-driven Few-shot Segmentation—0
Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation—0
C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation—0
Incorporating Near-Infrared Information into Semantic Image Segmentation—0
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation—0
Incorporating prior knowledge in medical image segmentation: a survey—0
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks—0
Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling—0
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation—0
Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow—0
Incremental Class Discovery for Semantic Segmentation with RGBD Sensing—0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation—0
Incremental Learning for Fully Unsupervised Word Segmentation Using Penalized Likelihood and Model Selection—0
Segmentation of the Left Ventricle by SDD double threshold selection and CHT—0
Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets—0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified