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 4351–4400 of 5035 papers

TitleStatusHype
Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images—0
Image Segmentation using Unsupervised Watershed Algorithm with an Over-segmentation Reduction Technique—0
Image segmentation via Cellular Automata—0
Image Segmentation via Divisive Normalization: dealing with environmental diversity—0
Image Segmentation via Probabilistic Graph Matching—0
Image Segmentation via Variational Model Based Tailored UNet: A Deep Variational Framework—0
Image Segmentation with Adaptive Spatial Priors from Joint Registration—0
Structure-Aware Image Segmentation with Homotopy Warping—0
Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems—0
Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors—0
Image segmentation with superpixel-based covariance descriptors in low-rank representation—0
Image Segmentation with transformers: An Overview, Challenges and Future—0
Image segmentation with traveling waves in an exactly solvable recurrent neural network—0
Image Semantic Relation Generation—0
Image Shadow Removal Using End-to-End Deep Convolutional Neural Networks—0
Image Translation-Based Unsupervised Cross-Modality Domain Adaptation for Medical Image Segmentation—0
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning—0
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices—0
Implicit Modeling -- A Generalization of Discriminative and Generative Approaches—0
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation—0
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation—0
Implicit U-Net for volumetric medical image segmentation—0
Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving—0
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study—0
Improved Inference via Deep Input Transfer—0
Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism—0
Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism—0
Improved Workflow for Unsupervised Multiphase Image Segmentation—0
Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients—0
Improving Brain Magnetic Resonance Image MRI Segmentation via a Novel Algorithm based on Genetic and Regional Growth—0
Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation—0
Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks—0
Improving FHB Screening in Wheat Breeding Using an Efficient Transformer Model—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
Improving Gibbs Sampler Scan Quality with DoGS—0
Improving Image co-segmentation via Deep Metric Learning—0
Improving Learning Effectiveness For Object Detection and Classification in Cluttered Backgrounds—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Improving Multi-Modal Learning with Uni-Modal Teachers—0
Improving RANSAC-Based Segmentation Through CNN Encapsulation—0
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation—0
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context—0
Improving Training on Noisy Stuctured Labels—0
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation—0
In-context learning for medical image segmentation—0
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation—0
Incorporating Near-Infrared Information into Semantic Image Segmentation—0
Incorporating prior knowledge in medical image segmentation: a survey—0
Incremental Class Discovery for Semantic Segmentation with RGBD Sensing—0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI 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