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Segmentation

Papers

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Showing 11551–11575 of 13072 papers

TitleStatusHype
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation—0
Lung nodules segmentation from CT with DeepHealth toolkit—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Lung Segmentation in Chest X-rays with Res-CR-Net—0
Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Adversarial Networks for the Detection of Aggressive Prostate Cancer—0
LVIC: Multi-modality segmentation by Lifting Visual Info as Cue—0
Adversarial Mutual Leakage Network for Cell Image Segmentation—0
LVIS Challenge Track Technical Report 1st Place Solution: Distribution Balanced and Boundary Refinement for Large Vocabulary Instance Segmentation—0
Adversarial Multi-Criteria Learning for Chinese Word Segmentation—0
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging—0
QANet -- Quality Assurance Network for Image Segmentation—0
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation—0
Adversarially robust segmentation models learn perceptually-aligned gradients—0
3D-BEVIS: Bird's-Eye-View Instance Segmentation—0
3D-Aware Instance Segmentation and Tracking in Egocentric Videos—0
M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation—0
3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement—0
Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation—0
M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation—0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans—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