SOTAVerified

Image Segmentation

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

Papers

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Showing 3951–4000 of 5035 papers

TitleStatusHype
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation—0
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input—0
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity—0
Multi-source adversarial transfer learning based on similar source domains with local features—0
Progressive Curriculum Learning with Scale-Enhanced U-Net for Continuous Airway Segmentation—0
Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation—0
Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation—0
CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation—0
Multi Task Consistency Guided Source-Free Test-Time Domain Adaptation Medical Image Segmentation—0
Multi-task deep learning for image segmentation using recursive approximation tasks—0
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation—0
Multi-task Fusion for Efficient Panoptic-Part Segmentation—0
Reducing the Model Variance of a Rectal Cancer Segmentation Network—0
Multi-Task Neural Processes—0
Multi-Task Self-Supervised Learning for Image Segmentation Task—0
Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation—0
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions—0
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography—0
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors—0
Multi-view Remote Sensing Image Segmentation With SAM priors—0
Multi-view Spectral Polarization Propagation for Video Glass Segmentation—0
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation—0
Mutual Evidential Deep Learning for Medical Image Segmentation—0
Mutual information deep regularization for semi-supervised segmentation—0
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images—0
NARF24: Estimating Articulated Object Structure for Implicit Rendering—0
Natural Scene Image Segmentation Based on Multi-Layer Feature Extraction—0
Navigating Uncertainty in Medical Image Segmentation—0
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images—0
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation—0
Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network—0
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation—0
ν-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters—0
Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks—0
Network-Agnostic Knowledge Transfer for Medical Image Segmentation—0
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation—0
Neural Architecture Search in operational context: a remote sensing case-study—0
Neural Diffusion Distance for Image Segmentation—0
Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images—0
A Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points—0
Neural ODEs for Image Segmentation with Level Sets—0
Neural Radiance Projection—0
Neural Style Transfer for Remote Sensing—0
Neuroevolution of Self-Attention Over Proto-Objects—0
Neuro-Symbolic Spatio-Temporal Reasoning—0
Neutro-Connectedness Cut—0
New region force for variational models in image segmentation and high dimensional data clustering—0
Next Generation Multicuts for Semi-Planar Graphs—0
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks—0
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All datasetsMSD (Mirror Segmentation Dataset)PMDMAS3KPascal Panoptic PartsRMASPASCAL VOCHuTu 80MARIDACOCO val2017ImageNetMSD HeartOxfordPets

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.92—Unverified
2HetNetIoU0.83—Unverified
3PMDIoU0.82—Unverified
4SANetIoU0.8—Unverified
5MirrorNetIoU0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetIoU0.73—Unverified
2HetNetIoU0.69—Unverified
3SANetIoU0.67—Unverified
4PMDIoU0.66—Unverified
5MirrorNetIoU0.59—Unverified
#ModelMetricClaimedVerifiedStatus
1SAM2-UNetmIoU0.8—Unverified
2MAS-SAMmIoU0.79—Unverified
3MASNetmIoU0.74—Unverified
4ZoomNetmIoU0.74—Unverified
#ModelMetricClaimedVerifiedStatus
1HIPIE (ViT-H)mIoUPartS63.8—Unverified
2PPSmIoUPartS58.6—Unverified
3HIPIE (ResNet-50)mIoUPartS57.2—Unverified
4JPPFmIoUPartS54.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MAS-SAMmIoU0.74—Unverified
2SAM2-UNetmIoU0.74—Unverified
3MASNetmIoU0.73—Unverified
4ZoomNetmIoU0.73—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CmIoU63.6—Unverified
2OneNete,4-SmAP0.552.75—Unverified
3OneNeted,4mIoU14.9—Unverified
#ModelMetricClaimedVerifiedStatus
1UNetRDice0.98—Unverified
2PALEDDice0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttUNetIoU0.67—Unverified
2UNetIoU0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1SynCo (ResNet-50) 200epmask AP35.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileOne-S0GFLOPs0.28—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4mIoU6.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OneNete,4-CDice Score0.97—Unverified