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 2551–2600 of 5035 papers

TitleStatusHype
Deep Learning for Medical Image Segmentation—0
Expert-Adaptive Medical Image Segmentation—0
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities—0
Deep Learning for Object Saliency Detection and Image Segmentation—0
Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Deep Learning in Medical Ultrasound Image Segmentation: a Review—0
Deep Learning in Multi-organ Segmentation—0
Deep Learning Model with GA based Feature Selection and Context Integration—0
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation—0
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset—0
Deep Learning Training Procedure Augmentations—0
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans—0
Deeply Learning the Messages in Message Passing Inference—0
Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation—0
DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images—0
Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation—0
Deep Multiphase Level Set for Scene Parsing—0
Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation—0
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy—0
Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation—0
Deep Neural Patchworks: Coping with Large Segmentation Tasks—0
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception—0
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Deep Retinal Image Segmentation with Regularization Under Geometric Priors—0
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection—0
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels—0
Deep Semantic Segmentation of Natural and Medical Images: A Review—0
Deep Structural Causal Shape Models—0
Deep Superpixel Cut for Unsupervised Image Segmentation—0
Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation—0
Deep Tiered Image Segmentation For Detecting Internal Ice Layers in Radar Imagery—0
DeepTriNet: A Tri-Level Attention Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images—0
Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Dense 3D Visual Mapping via Semantic Simplification—0
Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation—0
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation—0
Dense Self-Supervised Learning for Medical Image Segmentation—0
Dense Semantic Image Segmentation with Objects and Attributes—0
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation—0
DenseVLM: A Retrieval and Decoupled Alignment Framework for Open-Vocabulary Dense Prediction—0
DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation—0
Depth-wise layering of 3d images using dense depth maps: a threshold based approach—0
Derivate-based Component-Trees for Multi-Channel Image Segmentation—0
Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal—0
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images—0
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation—0
Detecting Abnormal Health Conditions in Smart Home Using a Drone—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