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Segmentation

Papers

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Showing 7226–7250 of 13072 papers

TitleStatusHype
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation—0
A Study on the Use of Edge TPUs for Eye Fundus Image Segmentation—0
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time CapabilityCode0
Exploring Generalizable Distillation for Efficient Medical Image SegmentationCode0
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation—0
ConvUNeXt: An efficient convolution neural network for medical image segmentationCode0
Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes—0
BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentationCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Latent Space Unsupervised Semantic Segmentation—0
Deep Statistic Shape Model for Myocardium Segmentation—0
COBRA: Cpu-Only aBdominal oRgan segmentAtion—0
Region Aware Video Object Segmentation with Deep Motion Modeling—0
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation—0
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion ImagingCode0
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations—0
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
Large-Kernel Attention for 3D Medical Image Segmentation—0
KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image—0
Segmentation of 3D Dental Images Using Deep Learning—0
Global and Local Features through Gaussian Mixture Models on Image Semantic SegmentationCode0
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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