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 3401–3450 of 5035 papers

TitleStatusHype
Source Identification: A Self-Supervision Task for Dense Prediction—0
SP ^3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation—0
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation—0
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images—0
Sparse Depth Super Resolution—0
Sparse Norm Filtering—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation—0
Spatial Correspondence between Graph Neural Network-Segmented Images—0
Spatial distance dependent Chinese restaurant processes for image segmentation—0
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation—0
Spatial Semantic Recurrent Mining for Referring Image Segmentation—0
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data—0
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)—0
Spatio-Temporal Dual-Stream Neural Network for Sequential Whole-Body PET Segmentation—0
SPCTNet: A Series-Parallel CNN and Transformer Network for 3D Medical Image Segmentation—0
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation—0
SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery—0
Spectral Clustering with Jensen-type kernels and their multi-point extensions—0
Spectral Graph Cut from a Filtering Point of View—0
Spectral GUI for Automated Tissue and Lesion Segmentation of T1 Weighted Breast MR Images—0
Spectral Image Segmentation with Global Appearance Modeling—0
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
SpectralZoom: Efficient Segmentation with an Adaptive Hyperspectral Camera—0
SPF-Net: Solar panel fault detection using U-Net based deep learning image classification—0
Spherical Feature Pyramid Networks For Semantic Segmentation—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
SPT: Sequence Prompt Transformer for Interactive Image Segmentation—0
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model—0
SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data—0
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model—0
SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation—0
Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup—0
Stagnant zone segmentation with U-net—0
STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery—0
State-of-The-Art Fuzzy Active Contour Models for Image Segmentation—0
Static Segmentation by Tracking: A Frustratingly Label-Efficient Approach to Fine-Grained Segmentation—0
Stereo-based terrain traversability analysis using normal-based segmentation and superpixel surface analysis—0
StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images—0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation—0
STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation—0
Stopping Criterion for the Mean Shift Iterative Algorithm—0
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models—0
Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution—0
StructBoost: Boosting Methods for Predicting Structured Output Variables—0
Semantic and structural image segmentation for prosthetic vision—0
Structure Boundary Preserving Segmentation for Medical Image With Ambiguous Boundary—0
Structured IB: Improving Information Bottleneck with Structured Feature Learning—0
Structured Learning of Tree Potentials in CRF for Image Segmentation—0
Structured Prediction with Abstention via the Lovász Hinge—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