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Segmentation

Papers

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Showing 8951–9000 of 13072 papers

TitleStatusHype
Quality Control in Crowdsourced Object Segmentation—0
Quality monitoring of federated Covid-19 lesion segmentation—0
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT—0
Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation—0
Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria—0
Quantifying Model Uncertainty for Semantic Segmentation using Operators in the RKHS—0
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation—0
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection—0
Quantity beats quality for semantic segmentation of corrosion in images—0
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation—0
Quantized Feature Distillation for Network Quantization—0
Quantum-enhanced unsupervised image segmentation for medical images analysis—0
Quantum Image Segmentation Based on Grayscale Morphology—0
Quantum Motion Segmentation—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge—0
Query by String word spotting based on character bi-gram indexing—0
Query-guided Prototype Evolution Network for Few-Shot Segmentation—0
Query Refinement Transformer for 3D Instance Segmentation—0
QueSTMaps: Queryable Semantic Topological Maps for 3D Scene Understanding—0
Quickest Moving Object Detection—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception—0
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild—0
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation—0
R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation—0
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds—0
Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV—0
RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging—0
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods—0
Radiomic Feature Stability Analysis based on Probabilistic Segmentations—0
Radiomics as a measure superior to the Dice similarity coefficient for tumor segmentation performance evaluation—0
Radious: Unveiling the Enigma of Dental Radiology with BEIT Adaptor and Mask2Former in Semantic Segmentation—0
RadSAM: Segmenting 3D radiological images with a 2D promptable model—0
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
RAFT: Robust Augmentation of FeaTures for Image Segmentation—0
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
Rain rendering for evaluating and improving robustness to bad weather—0
Rainy screens: Collecting rainy datasets, indoors—0
RAIS: Robust and Accurate Interactive Segmentation via Continual Learning—0
Random 2.5D U-net for Fully 3D Segmentation—0
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization—0
Random Forest with Learned Representations for Semantic Segmentation—0
Randomized Greedy Inference for Joint Segmentation, POS Tagging and Dependency Parsing—0
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information—0
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds—0
Ranking pre-trained segmentation models for zero-shot transferability—0
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving—0
RA V-Net: Deep learning network for automated liver 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