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 4951–5000 of 5035 papers

TitleStatusHype
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation—0
Patch-Based Deep Unsupervised Image Segmentation using Graph Cuts—0
Patch-based Evaluation of Image Segmentation—0
Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks—0
Patch Network for medical image Segmentation—0
Pathological Image Segmentation with Noisy Labels—0
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation—0
Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation—0
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation—0
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
PCCA-Model: an attention module for medical image segmentation—0
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data—0
Peekaboo - Where are the Objects? Structure Adjusting Superpixels—0
PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation—0
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation—0
Penalizing small errors using an Adaptive Logarithmic Loss—0
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan—0
Performance Analysis of UNet and Variants for Medical Image Segmentation—0
Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation—0
Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg—0
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images—0
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs—0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation—0
PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation—0
PGD-UNet: A Position-Guided Deformable Network for Simultaneous Segmentation of Organs and Tumors—0
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation—0
Phase Collaborative Network for Two-Phase Medical Image Segmentation—0
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection—0
Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation—0
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation—0
PIDformer: Transformer Meets Control Theory—0
Piecewise Flat Embedding for Image Segmentation—0
PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation—0
PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module—0
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence—0
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors—0
Pitfalls of topology-aware image segmentation—0
Pixel-Level Clustering Network for Unsupervised Image Segmentation—0
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks—0
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos—0
Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation—0
Pixel-wise Modulated Dice Loss for Medical Image Segmentation—0
Planar Ultrametric Rounding for Image Segmentation—0
Planar Ultrametrics for Image Segmentation—0
Planecell: Representing the 3D Space with Planes—0
Plant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage—0
Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall—0
PlateSegFL: A Privacy-Preserving License Plate Detection Using Federated Segmentation Learning—0
Play and Learn: Using Video Games to Train Computer Vision Models—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