SOTAVerified

Segmentation

Papers

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Showing 9626–9650 of 13072 papers

TitleStatusHype
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation—0
Seg&Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing—0
SegTAD: Precise Temporal Action Detection via Semantic Segmentation—0
SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing—0
SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation—0
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images—0
SegTime: Precise Time Series Segmentation without Sliding Window—0
SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations—0
Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels—0
Select, Attend, and Transfer: Light, Learnable Skip Connections—0
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation—0
Selective Prediction for Semantic Segmentation using Post-Hoc Confidence Estimation and Its Performance under Distribution Shift—0
Selective Segmentation Networks Using Top-Down Attention—0
Selective Video Object Cutout—0
Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical Image Segmentation—0
Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein—0
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation—0
Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging—0
Self-calibrated convolution towards glioma segmentation—0
Self-Configuring and Evolving Fuzzy Image Thresholding—0
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation—0
Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation—0
Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery—0
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models—0
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning—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