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Semantic Segmentation

Papers

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Showing 8976–9000 of 14763 papers

TitleStatusHype
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