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Segmentation

Papers

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Showing 7426–7450 of 13072 papers

TitleStatusHype
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation—0
Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)—0
FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization—0
Application of the Ring Theory in the Segmentation of Digital Images—0
A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet—0
Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora—0
Multi-focus Image Fusion Based on Similarity Characteristics—0
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging—0
Diffusion Models for Open-Vocabulary Segmentation—0
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models—0
Multi-domain semantic segmentation with pyramidal fusion—0
DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication—0
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds—0
Federated Unsupervised Semantic Segmentation—0
Application of the Hidden Markov Model for determining PQRST complexes in electrocardiograms—0
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches—0
DifNet: Semantic Segmentation by Diffusion Networks—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Midas Loop: A Prioritized Human-in-the-Loop Annotation for Large Scale Multilayer Data—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
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All datasetsSA-1B.MFSDMMFloodSimGas

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8—Unverified
2SAMAverage Precision38.9—Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20—Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69—Unverified