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 4601–4650 of 5035 papers

TitleStatusHype
Locate then Segment: A Strong Pipeline for Referring Image Segmentation—0
Location Dependent Dirichlet Processes—0
LOGCAN++: Adaptive Local-global class-aware network for semantic segmentation of remote sensing imagery—0
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes—0
Loss Max-Pooling for Semantic Image Segmentation—0
Low-light Enhancement Method Based on Attention Map Net—0
Low-Rank Mixture-of-Experts for Continual Medical Image Segmentation—0
LQMFormer: Language-aware Query Mask Transformer for Referring Image Segmentation—0
LSegDiff: A Latent Diffusion Model for Medical Image Segmentation—0
LSM: Learning Subspace Minimization for Low-level Vision—0
Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging—0
L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation—0
LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images—0
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation—0
Lumos in the Night Sky: AI-enabled Visual Tool for Exploring Night-Time Light Patterns—0
Lung Diseases Image Segmentation using Faster R-CNNs—0
Lung image segmentation by generative adversarial networks—0
Lung nodules segmentation from CT with DeepHealth toolkit—0
M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation—0
Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images—0
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications—0
MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation—0
MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance—0
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network—0
Mammogram Edge Detection Using Hybrid Soft Computing Methods—0
Mapping Vulnerable Populations with AI—0
MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation—0
Marginal Thresholding in Noisy Image Segmentation—0
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models—0
RISAM: Referring Image Segmentation via Mutual-Aware Attention Features—0
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation—0
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation—0
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
MaskRange: A Mask-classification Model for Range-view based LiDAR Segmentation—0
MaskSAM: Towards Auto-prompt SAM with Mask Classification for Medical Image Segmentation—0
MaskUno: Switch-Split Block For Enhancing Instance Segmentation—0
MASNet:Improve Performance of Siamese Networks with Mutual-attention for Remote Sensing Change Detection Tasks—0
Massively Parallel Benders Decomposition for Correlation Clustering—0
Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net—0
SAM^Med: A medical image annotation framework based on large vision model—0
Matlab Implementation of Machine Vision Algorithm on Ballast Degradation Evaluation—0
MA-Unet: An improved version of Unet based on multi-scale and attention mechanism for medical image segmentation—0
Maximin affinity learning of image segmentation—0
DRIFTS: Optimizing Domain Randomization with Synthetic Data and Weight Interpolation for Fetal Brain Tissue Segmentation—0
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation—0
Mayfly optimization with deep learning enabled retinal fundus image classification model—0
MCICSAM: Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone 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