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Segmentation

Papers

Showing 25012525 of 13072 papers

TitleStatusHype
FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-pickingCode1
Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image SegmentationCode1
Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud DatasetCode1
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object SegmentationCode1
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic SegmentationCode1
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
SegGroup: Seg-Level Supervision for 3D Instance and Semantic SegmentationCode1
Point TransformerCode1
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene ContextsCode1
docExtractor: An off-the-shelf historical document element extractionCode1
Real-Time Polyp Segmentation Using U-Net with IoU LossCode1
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPUCode1
OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography StudyCode1
Simple Copy-Paste is a Strong Data Augmentation Method for Instance SegmentationCode1
Meticulous Object SegmentationCode1
Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid ConsistencyCode1
Classifying Breast Histopathology Images with a Ductal Instance-Oriented PipelineCode1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learningCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Amodal Segmentation Based on Visible Region Segmentation and Shape PriorCode1
Full-Glow: Fully conditional Glow for more realistic image generationCode1
HRCenterNet: An Anchorless Approach to Chinese Character Segmentation in Historical DocumentsCode1
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CTCode1
ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic SegmentationCode1
Annotation-efficient deep learning for automatic medical image 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