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Semantic Segmentation

Papers

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Showing 8951–9000 of 14763 papers

TitleStatusHype
Transductive image segmentation: Self-training and effect of uncertainty estimation—0
TranSegPGD: Improving Transferability of Adversarial Examples on Semantic Segmentation—0
Transesophageal Echocardiography Generation using Anatomical Models—0
Transferability Estimation for Semantic Segmentation Task—0
Transferability Estimation using Bhattacharyya Class Separability—0
Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images—0
Transferability Metrics for Selecting Source Model Ensembles—0
Webly Supervised Semantic Segmentation—0
Transferable Semi-supervised Semantic Segmentation—0
Muographic Image Upsampling with Machine Learning for Built Infrastructure Applications—0
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation—0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Transfer Learning for Brain Tumor Segmentation—0
Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm—0
Multi-view Spectral Polarization Propagation for Video Glass Segmentation—0
Transfer Learning from Partial Annotations for Whole Brain Segmentation—0
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod—0
WebSeg: Learning Semantic Segmentation from Web Searches—0
Multi-view Remote Sensing Image Segmentation With SAM priors—0
Transferring and Regularizing Prediction for Semantic Segmentation—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Transferring Knowledge for Food Image Segmentation using Transformers and Convolutions—0
Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming—0
Transferring Styles for Reduced Texture Bias and Improved Robustness in Semantic Segmentation Networks—0
Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging—0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Multi-view PointNet for 3D Scene Understanding—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras—0
Multiview Cross-supervision for Semantic Segmentation—0
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development—0
Transformer-based Flood Scene Segmentation for Developing Countries—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors—0
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation—0
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students—0
Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation—0
Multiview Based 3D Scene Understanding On Partial Point Sets—0
Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography—0
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions—0
Transformer Scale Gate for Semantic Segmentation—0
Multi-Temporal Aerial Image Registration Using Semantic Features—0
Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation—0
Transformers Meet Visual Learning Understanding: A Comprehensive Review—0
Multi-Task Self-Supervised Learning for Image Segmentation Task—0
Transformer-Unet: Raw Image Processing with Unet—0
Transformer Utilization in Medical Image Segmentation Networks—0
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation—0
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives—0
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All datasetsADE20KNYU-Depth V2Cityscapes testCityscapes valADE20K valPASCAL ContextS3DIS Area5PASCAL VOC 2012 testS3DISScanNetSUN-RGBDDensePASS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternImage-H (M3I Pre-training)Params (M)1,310—Unverified
2ViT-P (InternImage-H)Validation mIoU63.6—Unverified
3ONE-PEACEValidation mIoU63—Unverified
4M3I Pre-training (InternImage-H)Validation mIoU62.9—Unverified
5InternImage-HValidation mIoU62.9—Unverified
6BEiT-3Validation mIoU62.8—Unverified
7EVAValidation mIoU62.3—Unverified
8ViT-P (OneFormer, InternImage-H)Validation mIoU61.6—Unverified
9ViT-Adapter-L (Mask2Former, BEiTv2 pretrain)Validation mIoU61.5—Unverified
10FD-SwinV2-GValidation mIoU61.4—Unverified