SOTAVerified

Segmentation

Papers

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Showing 9876–9900 of 13072 papers

TitleStatusHype
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation—0
Transforming the Interactive Segmentation for Medical Imaging—0
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers—0
TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation—0
TransLandSeg: A Transfer Learning Approach for Landslide Semantic Segmentation Based on Vision Foundation Model—0
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network—0
Brain tumour segmentation with incomplete imaging data—0
Transparency Distortion Robustness for SOTA Image Segmentation Tasks—0
TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation—0
TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery—0
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris—0
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction—0
Tree-Cut for Probabilistic Image Segmentation—0
Tree Instance Segmentation With Temporal Contour Graph—0
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning—0
Tree-NET: Enhancing Medical Image Segmentation Through Efficient Low-Level Feature Training—0
TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation—0
Tree semantic segmentation from aerial image time series—0
TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation—0
TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors—0
Trilateral Attention Network for Real-time Medical Image Segmentation—0
Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images—0
MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling—0
Triply Supervised Decoder Networks for Joint Detection and Segmentation—0
TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images—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