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Zero Shot Segmentation

Papers

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Showing 101–125 of 134 papers

TitleStatusHype
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation—0
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data—0
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data—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
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—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
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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