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Semantic Segmentation

Papers

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Showing 9376–9400 of 14763 papers

TitleStatusHype
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation—0
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation—0
Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication—0
Unsupervised 3D Point Cloud Representation Learning by Triangle Constrained Contrast for Autonomous Driving—0
Meta Corrupted Pixels Mining for Medical Image Segmentation—0
Unsupervised Adaptation of Semantic Segmentation Models without Source Data—0
Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint—0
White Matter Hyperintensities Segmentation Using Probabilistic TransUNet—0
Unsupervised augmentation optimization for few-shot medical image segmentation—0
Unsupervised Automated Event Detection using an Iterative Clustering based Segmentation Approach—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps—0
Multi-focus Image Fusion Based on Similarity Characteristics—0
Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation—0
Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks—0
Unsupervised Class Generation to Expand Semantic Segmentation Datasets—0
Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology—0
Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity—0
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation—0
Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation—0
Medical Image Segmentation on MRI Images with Missing Modalities: A Review—0
Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images—0
Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering—0
Unsupervised Discovery of Object-Centric Neural Fields—0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision—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