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 2851–2900 of 5035 papers

TitleStatusHype
Fast Constraint Propagation for Image Segmentation—0
Fast Edge Detection Using Structured Forests—0
Faster Multi-Object Segmentation Using Parallel Quadratic Pseudo-Boolean Optimization—0
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes—0
Fast Marching Energy CNN—0
Fast probabilistic snake algorithm—0
Fast Recurrent Fully Convolutional Networks for Direct Perception in Autonomous Driving—0
Fast Semantic Image Segmentation with High Order Context and Guided Filtering—0
Fast Submodular Function Maximization—0
Fast unsupervised Bayesian image segmentation with adaptive spatial regularisation—0
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision—0
FBNet: Feature Balance Network for Urban-Scene Segmentation—0
FCSN: Global Context Aware Segmentation by Learning the Fourier Coefficients of Objects in Medical Images—0
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation—0
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image—0
Feature Decoupling-Recycling Network for Fast Interactive Segmentation—0
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation—0
Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation—0
Feature Fusion through Multitask CNN for Large-scale Remote Sensing Image Segmentation—0
Feature Selective Transformer for Semantic Image Segmentation—0
Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation—0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation—0
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated Data Model—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Unsupervised Semantic Segmentation—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Feedback Attention for Cell Image Segmentation—0
Feedback Chain Network For Hippocampus Segmentation—0
Feedback U-net for Cell Image Segmentation—0
Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code—0
Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning—0
FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything Capability for Versatile Image Segmentation—0
Fewer is More: Image Segmentation Based Weakly Supervised Object Detection with Partial Aggregation—0
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation—0
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation—0
Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion—0
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation—0
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks—0
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning—0
Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding—0
Few-shot Medical Image Segmentation with Cycle-resemblance Attention—0
Few-Shot Medical Image Segmentation with Large Kernel Attention—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation—0
FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention—0
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference—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