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Semantic Segmentation

Papers

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Showing 5376–5400 of 14763 papers

TitleStatusHype
AinnoSeg: Panoramic Segmentation with High Perfomance—0
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
Road Segmentation with Image-LiDAR Data Fusion—0
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss—0
High-resolution LIDAR-based Depth Mapping using Bilateral Filter—0
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images—0
Automated Design of Salient Object Detection Algorithms with Brain Programming—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network—0
AIMS: All-Inclusive Multi-Level Segmentation—0
High-Resolution Multispectral Dataset for Semantic Segmentation—0
High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage—0
HiMo: High-Speed Objects Motion Compensation in Point Clouds—0
How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation—0
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images—0
AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines—0
High Quality Entity Segmentation—0
High Quality Segmentation for Ultra High-resolution Images—0
CSS-Segment: 2nd Place Report of LSVOS Challenge VOS Track—0
CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation—0
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen—0
CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation—0
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy—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