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Semantic Segmentation

Papers

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Showing 9326–9350 of 14763 papers

TitleStatusHype
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks—0
Multi-Class Micro-CT Image Segmentation Using Sparse Regularized Deep Networks—0
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy—0
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing—0
Multi-Class Segmentation of Heterogeneous Areas in Biomedical and Environmental Images Based on the Assessment of Local Edge Density—0
Multiclass Segmentation using Teeth Attention Modules for Dental X-ray Images—0
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks—0
Multiclass Semantic Segmentation to Identify Anatomical Sub-Regions of Brain and Measure Neuronal Health in Parkinson's Disease—0
Multiclass Semantic Video Segmentation With Object-Level Active Inference—0
MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages—0
Multi-Contrast MRI Segmentation Trained on Synthetic Images—0
Multicuts and Perturb & MAP for Probabilistic Graph Clustering—0
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation—0
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes—0
Multi-Depth Fusion Network for Whole-Heart CT Image Segmentation—0
Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation—0
Multi-dimensional Parametric Mincuts for Constrained MAP Inference—0
Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI—0
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation—0
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation—0
Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning—0
Multi-domain semantic segmentation with pyramidal fusion—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning—0
Multi-Exit Semantic Segmentation Networks—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