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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4526–4550 of 5035 papers

TitleStatusHype
Learning Affinity via Spatial Propagation Networks—0
Learning Affinity via Spatial Propagation Network—0
Learning Robust Medical Image Segmentation from Multi-source Annotations—0
Linguistic Query-Guided Mask Generation for Referring Image Segmentation—0
Learning a sparse database for patch-based medical image segmentation—0
Learning-Based Atlas Selection for Multiple-Atlas Segmentation—0
Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative—0
Learning-Based Keypoint Registration for Fetoscopic Mosaicking—0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation—0
Learning Discriminators as Energy Networks in Adversarial Learning—0
Learning Disentangled Representations of Satellite Image Time Series—0
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets—0
Learning from Exemplars for Interactive Image Segmentation—0
Learning from Partial Label Proportions for Whole Slide Image Segmentation—0
Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift—0
Visually Consistent Hierarchical Image Classification—0
Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation—0
Learning Instance-Specific Adaptation for Cross-Domain Segmentation—0
Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images—0
Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation—0
Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation—0
Learning Object Interactions and Descriptions for Semantic Image Segmentation—0
Learning pose variations within shape population by constrained mixtures of factor analyzers—0
Learning Probabilistic Topological Representations Using Discrete Morse Theory—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