SOTAVerified

Segmentation

Papers

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Showing 9601–9650 of 13072 papers

TitleStatusHype
Segmenting Dead Sea Scroll Fragments for a Scientific Image Set—0
Segmenting Dermoscopic Images—0
Segmenting Maxillofacial Structures in CBCT Volumes—0
Segmenting Medical Images: From UNet to Res-UNet and nnUNet—0
Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask—0
Segmenting Messy Text: Detecting Boundaries in Text Derived from Historical Newspaper Images—0
Segmenting Natural Language Sentences via Lexical Unit Analysis—0
Segmenting Numerical Substitution Ciphers—0
Segmenting Scenes by Matching Image Composites—0
Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net—0
Segmenting Subtitles for Correcting ASR Segmentation Errors—0
Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database—0
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision—0
Segment then Splat: A Unified Approach for 3D Open-Vocabulary Segmentation based on Gaussian Splatting—0
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation—0
Segment Using Just One Example—0
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation—0
SegNeRF: 3D Part Segmentation with Neural Radiance Fields—0
SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks—0
SegPoint: Segment Any Point Cloud via Large Language Model—0
SegPrompt: Using Segmentation Map as a Better Prompt to Finetune Deep Models for Kidney Stone Classification—0
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images—0
Segregation and Context Aggregation Network for Real-time Cloud Segmentation—0
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization—0
SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation—0
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