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Semantic Segmentation

Papers

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Showing 5301–5325 of 14763 papers

TitleStatusHype
CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx—0
Automated risk classification of colon biopsies based on semantic segmentation of histopathology images—0
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images—0
Automated Resolution Selection for Image Segmentation—0
Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products—0
Hierarchical interaction network for video object segmentation from referring expressions—0
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation—0
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study—0
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data—0
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation—0
CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM—0
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
Hierarchical Instance Mixing across Domains in Aerial Segmentation—0
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation—0
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning—0
Automated National Urban Map Extraction—0
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling—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 Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract—0
Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation—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