SOTAVerified

Segmentation

Papers

Showing 651675 of 13072 papers

TitleStatusHype
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
Active learning for medical image segmentation with stochastic batchesCode1
Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant MetricsCode1
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part SegmentationCode1
ContrastMask: Contrastive Learning to Segment Every ThingCode1
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image SegmentationCode1
Cooperative Self-Training for Multi-Target Adaptive Semantic SegmentationCode1
Autoencoder-based background reconstruction and foreground segmentation with background noise estimationCode1
AutoFocusFormer: Image Segmentation off the GridCode1
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-NetCode1
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation TrackCode1
Active Boundary Loss for Semantic SegmentationCode1
Auto-Compressing Subset Pruning for Semantic Image SegmentationCode1
Contrastive Registration for Unsupervised Medical Image SegmentationCode1
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image SegmentationCode1
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic SegmentationCode1
Contour Proposal Networks for Biomedical Instance SegmentationCode1
Contrastive Boundary Learning for Point Cloud SegmentationCode1
ActionVOS: Actions as Prompts for Video Object SegmentationCode1
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task IntegrationCode1
ContourFormer:Real-Time Contour-Based End-to-End Instance Segmentation TransformerCode1
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse DataCode1
Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETRCode1
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Benchmark Results

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