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Zero Shot Segmentation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 134 papers

TitleStatusHype
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI—0
Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images—0
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation—0
PaveSAM Segment Anything for Pavement Distress—0
Image-to-Lidar Relational Distillation for Autonomous Driving Data—0
SAM-SP: Self-Prompting Makes SAM Great Again—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation—0
PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI EstimationCode0
X-Recon: Learning-based Patient-specific High-Resolution CT Reconstruction from Orthogonal X-Ray ImagesCode0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
PanSAM: Zero-Shot, Prompt-Free Pancreas Segmentation in CT ImagingCode0
PanopticRecon: Leverage Open-vocabulary Instance Segmentation for Zero-shot Panoptic Reconstruction—0
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Interpreting the Second-Order Effects of Neurons in CLIP—0
SimSAM: Zero-shot Medical Image Segmentation via Simulated InteractionCode0
Open-Vocabulary SAM3D: Towards Training-free Open-Vocabulary 3D Scene Understanding—0
SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model—0
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery—0
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
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All datasetsSegmentation in the WildADE20K training-free zero-shot segmentation

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Grounded HQ-SAMMean AP49.6—Unverified
2Grounded-SAMMean AP46—Unverified
3UNINEXTMean AP42.1—Unverified
4HIPIEMean AP41.6—Unverified
5SANMean AP41.4—Unverified
6odiseMean AP38.7—Unverified
7OpenSEEDMean AP36.1—Unverified
8OpenSDMean AP35.8—Unverified
9SGinW_Team (X-Decoder-L)Mean AP32.2—Unverified
10SGinW_Team (X-Decoder-B)Mean AP27.7—Unverified
#ModelMetricClaimedVerifiedStatus
1COSMOS ViT-B/16mIoU17.7—Unverified
2GEM (MetaCLIP)mIoU17.1—Unverified
3GEM (CLIP)mIoU15.7—Unverified
4CLIPSurgerymIoU12.9—Unverified
5MaskCLIPmIoU10.2—Unverified