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 2901–2950 of 5035 papers

TitleStatusHype
Waymo Open Dataset: Panoramic Video Panoptic Segmentation—0
Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity—0
Weakly-Supervised Dual Clustering for Image Semantic Segmentation—0
Weakly-supervised fire segmentation by visualizing intermediate CNN layers—0
Weakly Supervised Instance Segmentation Using Hybrid Network—0
Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma Images—0
Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound—0
Weakly Supervised Learning of Affordances—0
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss—0
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM—0
Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency—0
Weakly Supervised Segmentation by A Deep Geodesic Prior—0
Weakly-supervised segmentation of referring expressions—0
Weakly Supervised Segmentation of Small Buildings With Point Labels—0
Semi-Supervised Semantic Image Segmentation with Self-correcting Networks—0
Weakly-Supervised Semantic Segmentation using Motion Cues—0
Weakly Supervised Volumetric Image Segmentation with Deformed Templates—0
What Image Features Boost Housing Market Predictions?—0
What is Wrong with Continual Learning in Medical Image Segmentation?—0
What You Perceive Is What You Conceive: A Cognition-Inspired Framework for Open Vocabulary Image Segmentation—0
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation—0
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods—0
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation—0
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation—0
Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models—0
Wild Animal Classifier Using CNN—0
Winograd Convolution for Deep Neural Networks: Efficient Point Selection—0
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information—0
w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution—0
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography—0
Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion—0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing—0
Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation—0
Y-net: Biomedical Image Segmentation and Clustering—0
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark—0
Zero-shot Autonomous Microscopy for Scalable and Intelligent Characterization of 2D Materials—0
Zero-shot capability of SAM-family models for bone segmentation in CT scans—0
Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model—0
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study—0
Zero-Shot Gaze-based Volumetric Medical Image Segmentation—0
Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging—0
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs—0
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds—0
ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation—0
QANet -- Quality Assurance Network for Image Segmentation—0
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks—0
Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks—0
Region homogeneity in the Logarithmic Image Processing framework: application to region growing algorithms—0
Computing Valid p-values for Image Segmentation by Selective Inference—0
Generative Model-Based Ischemic Stroke Lesion 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