SOTAVerified

Segmentation

Papers

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Showing 5126–5150 of 13072 papers

TitleStatusHype
Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models—0
Comprehensive Multi-Modal Interactions for Referring Image Segmentation—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data—0
HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation—0
Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision—0
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study—0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle—0
Human Action Segmentation With Hierarchical Supervoxel Consistency—0
Human-centric Relation Segmentation: Dataset and Solution—0
Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images—0
Composite Statistical Inference for Semantic Segmentation—0
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation—0
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks—0
Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures—0
Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation—0
Assessing the Role of Random Forests in Medical Image Segmentation—0
HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation—0
Completing Visual Objects via Bridging Generation and Segmentation—0
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores—0
Assessing the generalization performance of SAM for ureteroscopy scene understanding—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