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 4101–4150 of 5035 papers

TitleStatusHype
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
Flame quality monitoring of flare stack based on deep visual features—0
Flow 2.0 -a flexible, scalable, cross-platform post-processing software for realtime phase contrast sequences—0
Flow Based Self-supervised Pixel Embedding for Image Segmentation—0
Flower Categorization using Deep Convolutional Neural Networks—0
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Focal Attention Networks: optimising attention for biomedical image segmentation—0
Foreground Clustering for Joint Segmentation and Localization in Videos and Images—0
Forest and Water Bodies Segmentation Through Satellite Images Using U-Net—0
ForestEyes Project: Conception, Enhancements, and Challenges—0
Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields—0
Foundation AI Model for Medical Image Segmentation—0
Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation—0
Foundation Models for Biomedical Image Segmentation: A Survey—0
Fourier Boundary Features Network with Wider Catchers for Glass Segmentation—0
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors—0
FPGA based Parallelized Architecture of Efficient Graph based Image Segmentation Algorithm—0
FP-PET: Large Model, Multiple Loss And Focused Practice—0
Fractional Calculus In Image Processing: A Review—0
Framework for lung CT image segmentation based on UNet++—0
FRAP analysis Measuring biophysical kinetic parameters using image analysis—0
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation—0
Free Form based active contours for image segmentation and free space perception—0
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models—0
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation—0
Freestyle Sketch-in-the-Loop Image Segmentation—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation—0
From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations—0
From CNN to Transformer: A Review of Medical Image Segmentation Models—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
From Explanations to Segmentation: Using Explainable AI for Image Segmentation—0
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels—0
From Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation—0
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures—0
Fully Aligned Network for Referring Image Segmentation—0
Fully Automated Organ Segmentation in Male Pelvic CT Images—0
Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation—0
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model—0
Fully-Automatic Synapse Prediction and Validation on a Large Data Set—0
Fully Connected Deep Structured Networks—0
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers—0
Fully Convolutional Neural Networks for Crowd Segmentation—0
Fully Hyperbolic Convolutional Neural Networks—0
Fully Transformer Networks for Semantic Image Segmentation—0
Function Norms and Regularization in Deep Networks—0
FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and Cup Segmentation in Fundus Images—0
Fuse4Seg: Image-Level Fusion Based Multi-Modality Medical Image Segmentation—0
Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation—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