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Semantic Segmentation

Papers

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Showing 8576–8600 of 14763 papers

TitleStatusHype
Feature Sharing Cooperative Network for Semantic Segmentation—0
FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation—0
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated Data Model—0
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections—0
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
Federated Multi-Target Domain Adaptation—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data—0
Federated Unsupervised Semantic Segmentation—0
FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Feedback Attention for Cell Image Segmentation—0
Feedback Chain Network For Hippocampus Segmentation—0
Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation—0
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation—0
Feedback U-net for Cell Image Segmentation—0
Feedbackward Decoding for Semantic Segmentation—0
Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code—0
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis—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