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Segmentation

Papers

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Showing 3801–3825 of 13072 papers

TitleStatusHype
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation—0
Distilling effective supervision for robust medical image segmentation with noisy labels—0
Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections—0
Distilling Facial Knowledge With Teacher-Tasks: Semantic-Segmentation-Features For Pose-Invariant Face-Recognition—0
Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity—0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation—0
Novel coronavirus pneumonia lesion segmentation in CT images—0
Amodal segmentation just like doing a jigsaw—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
Distributed Low-rank Subspace Segmentation—0
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation—0
Distributional Gaussian Processes Layers for Out-of-Distribution Detection—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019—0
Deep Learning Markov Random Field for Semantic Segmentation—0
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN—0
Distribution-aware Margin Calibration for Medical Image Segmentation—0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images—0
DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts—0
Deep Learning in Multi-organ Segmentation—0
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation—0
Automatic segmentation of MR brain images with a convolutional neural network—0
Active Label Refinement for Semantic Segmentation of Satellite Images—0
Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth 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