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Zero Shot Segmentation

Papers

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Showing 51–100 of 134 papers

TitleStatusHype
The Emergence of Objectness: Learning Zero-Shot Segmentation from VideosCode1
TomoSAM: a 3D Slicer extension using SAM for tomography segmentationCode1
TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-ShotCode1
Training-free Object Counting with PromptsCode1
ZegOT: Zero-shot Segmentation Through Optimal Transport of Text PromptsCode1
Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model EstimationCode1
Zero-Shot Tree Detection and Segmentation from Aerial Forest ImageryCode1
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
Testing the Segment Anything Model on radiology data—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models—0
Diffuse Attend and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion—0
Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning—0
3D Compositional Zero-shot Learning with DeCompositional Consensus—0
Image-to-Lidar Relational Distillation for Autonomous Driving Data—0
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
Interpreting the Second-Order Effects of Neurons in CLIP—0
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies—0
Consistent Structural Relation Learning for Zero-Shot Segmentation—0
Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images—0
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
AI-Driven Segmentation and Analysis of Microbial Cells—0
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI—0
Open-Vocabulary SAM3D: Towards Training-free Open-Vocabulary 3D Scene Understanding—0
Adapting a Segmentation Foundation Model for Medical Image Classification—0
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding—0
PanopticRecon: Leverage Open-vocabulary Instance Segmentation for Zero-shot Panoptic Reconstruction—0
PaveSAM Segment Anything for Pavement Distress—0
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery—0
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets—0
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation—0
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing—0
Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond—0
Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images—0
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation—0
SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation—0
SAM3D: Zero-Shot Semi-Automatic Segmentation in 3D Medical Images with the Segment Anything Model—0
SAM for Poultry Science—0
SAM-SP: Self-Prompting Makes SAM Great Again—0
SANeRF-HQ: Segment Anything for NeRF in High Quality—0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model—0
Segment Anything Meets Semantic Communication—0
Zero-shot spatial layout conditioning for text-to-image diffusion models—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