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Semantic Segmentation

Papers

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Showing 8826–8850 of 14763 papers

TitleStatusHype
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training—0
Semi-supervised Multi-task Learning for Semantics and Depth—0
Semi-supervised Node Splitting for Random Forest Construction—0
Semi-Supervised Normalized Cuts for Image Segmentation—0
Semi-Supervised Segmentation of Concrete Aggregate Using Consensus Regularisation and Prior Guidance—0
Semi-supervised segmentation of land cover images using nonlinear canonical correlation analysis with multiple features and t-SNE—0
Semi-Supervised Segmentation via Embedding Matching—0
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation—0
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes—0
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey—0
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment—0
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations—0
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning—0
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups—0
Semi Supervised Semantic Segmentation Using Generative Adversarial Network—0
Semi-supervised Semantic Segmentation using Auxiliary Network—0
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells—0
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data—0
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving—0
Semi-supervised Video Semantic Segmentation Using Unreliable Pseudo Labels for PVUW2024—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