SOTAVerified

Segmentation

Papers

Showing 626650 of 13072 papers

TitleStatusHype
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression SegmentationCode1
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional NetworksCode1
Activity Grammars for Temporal Action SegmentationCode1
3D Spatial Recognition without Spatially Labeled 3DCode1
Zero-Shot Semantic SegmentationCode1
Conditional Boundary Loss for Semantic SegmentationCode1
Condition-Invariant Semantic SegmentationCode1
Active Pointly-Supervised Instance SegmentationCode1
Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image SegmentationCode1
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analysesCode1
Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scansCode1
Comprehensive segmentation of deep grey nuclei from structural MRI dataCode1
Complete Instances Mining for Weakly Supervised Instance SegmentationCode1
CompNet: Complementary Segmentation Network for Brain MRI ExtractionCode1
Complementary Network with Adaptive Receptive Fields for Melanoma SegmentationCode1
1st Place Solutions for OpenImage2019 -- Object Detection and Instance SegmentationCode1
Complementary Patch for Weakly Supervised Semantic SegmentationCode1
Compositor: Bottom-up Clustering and Compositing for Robust Part and Object SegmentationCode1
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing FlowCode1
CondNet: Conditional Classifier for Scene SegmentationCode1
Active learning for medical image segmentation with stochastic batchesCode1
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance SegmentationCode1
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patientsCode1
Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ SegmentationCode1
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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