SOTAVerified

Segmentation

Papers

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Showing 2776–2800 of 13072 papers

TitleStatusHype
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation ModelsCode1
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery—0
Pixel-Wise Recognition for Holistic Surgical Scene UnderstandingCode2
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and TrackingCode0
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—0
Text Region Multiple Information Perception Network for Scene Text Detection—0
OMG-Seg: Is One Model Good Enough For All Segmentation?Code5
RAP-SAM: Towards Real-Time All-Purpose Segment AnythingCode3
Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing ImageryCode0
Slicer Networks—0
Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera—0
P2Seg: Pointly-supervised Segmentation via Mutual Distillation—0
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask InpaintingCode2
XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection—0
A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI—0
Artificial Intelligence-based algorithms in medical image scan seg-mentation and intelligent visual-content generation -- a concise overview—0
M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans—0
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation—0
CT Liver Segmentation via PVT-based Encoding and Refined DecodingCode1
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRICode1
Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning—0
Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements—0
Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling—0
Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)—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