SOTAVerified

Semantic Segmentation

Papers

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Showing 5851–5875 of 14763 papers

TitleStatusHype
Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing—0
A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring—0
Global context reasoning for semantic segmentation of 3d point clouds—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
Context-Reinforced Semantic Segmentation—0
Improving FHB Screening in Wheat Breeding Using an Efficient Transformer Model—0
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images—0
Global Hierarchical Attention for 3D Point Cloud Analysis—0
Contextrast: Contextual Contrastive Learning for Semantic Segmentation—0
Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation—0
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data—0
Global-Local Medical SAM Adaptor Based on Full Adaption—0
Global-Local Propagation Network for RGB-D Semantic Segmentation—0
Globally Optimal Joint Image Segmentation and Shape Matching Based on Wasserstein Modes—0
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning—0
Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors—0
Context Propagation from Proposals for Semantic Video Object Segmentation—0
Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural Priors for Semantic Image Segmentation—0
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
Improving Data-Efficient Fossil Segmentation via Model Editing—0
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images—0
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias—0
Generative Map Priors for Collaborative BEV Semantic Segmentation—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—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