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Semantic Segmentation

Papers

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Showing 5326–5350 of 14763 papers

TitleStatusHype
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
Hierarchical semantic segmentation using modular convolutional neural networks—0
Automated Multiclass Cardiac Volume Segmentation and Model Generation—0
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation—0
A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color—0
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework—0
Hierarchical Semantic Segmentation using Psychometric Learning—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Cutting-Splicing data augmentation: A novel technology for medical image segmentation—0
Airway Tree Modeling Using Dual-channel 3D UNet 3+ with Vesselness Prior—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
AirRoom: Objects Matter in Room Reidentification—0
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation—0
Cut out the annotator, keep the cutout: better segmentation with weak supervision—0
Automated Measurements of Key Morphological Features of Human Embryos for IVF—0
Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning—0
Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients—0
Hierarchical Side-Tuning for Vision Transformers—0
High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation—0
Automated Log-Scale Quantization for Low-Cost Deep Neural Networks—0
AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers—0
CUSUM Filter for Brain Segmentation on DSC Perfusion MR Head Scans with Abnormal Brain Anatomy—0
Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning—0
Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery—0
3D Object Segmentation for Shelf Bin Picking by Humanoid with Deep Learning and Occupancy Voxel Grid Map—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