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Zero Shot Segmentation

Papers

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Showing 51–100 of 134 papers

TitleStatusHype
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized CutCode2
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
Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear StructuresCode1
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
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image SegmentationCode2
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation—0
MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image SegmentationCode2
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
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic DataCode0
From Generalization to Precision: Exploring SAM for Tool Segmentation in Surgical Environments—0
TV-SAM: Increasing Zero-Shot Segmentation Performance on Multimodal Medical Images Using GPT-4 Generated Descriptive Prompts Without Human AnnotationCode1
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation—0
MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large ModelCode1
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments—0
Diffuse Attend and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion—0
Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic SegmentationCode1
Testing the Segment Anything Model on radiology data—0
OpenSD: Unified Open-Vocabulary Segmentation and DetectionCode0
SANeRF-HQ: Segment Anything for NeRF in High Quality—0
Grounding Everything: Emerging Localization Properties in Vision-Language TransformersCode1
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation—0
Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning—0
GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure SegmentationCode1
Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)Code0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
SILC: Improving Vision Language Pretraining with Self-Distillation—0
Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types—0
Learning Mask-aware CLIP Representations for Zero-Shot SegmentationCode1
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for EchocardiographyCode1
Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model EstimationCode1
Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable DiffusionCode2
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond—0
TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-ShotCode1
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion ModelsCode2
Hierarchical Open-vocabulary Universal Image SegmentationCode2
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning—0
Training-free Object Counting with PromptsCode1
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging—0
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical ImagesCode1
Zero-shot spatial layout conditioning for text-to-image diffusion models—0
Primitive Generation and Semantic-related Alignment for Universal Zero-Shot SegmentationCode1
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models—0
TomoSAM: a 3D Slicer extension using SAM for tomography segmentationCode1
Segment Anything Meets Semantic Communication—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