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Zero Shot Segmentation

Papers

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Showing 1–50 of 134 papers

TitleStatusHype
Compress Any Segment Anything Model (SAM)Code1
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data—0
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging—0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models—0
Zero-Shot Tree Detection and Segmentation from Aerial Forest ImageryCode1
Removing Watermarks with Partial Regeneration using Semantic InformationCode0
Adapting a Segmentation Foundation Model for Medical Image Classification—0
AI-Driven Segmentation and Analysis of Microbial Cells—0
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic GapCode0
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data—0
Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images—0
SmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images—0
Eye on the Target: Eye Tracking Meets Rodent Tracking—0
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing—0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model—0
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive ReinforcementCode4
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models—0
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation—0
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation ModelsCode2
HOLa: HoloLens Object LabelingCode0
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast ObjectsCode0
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-trainingCode1
Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition—0
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI—0
3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object RearrangementCode2
ZIM: Zero-Shot Image Matting for AnythingCode3
Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images—0
Interpreting and Editing Vision-Language Representations to Mitigate HallucinationsCode2
MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image SegmentationCode2
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation—0
PaveSAM Segment Anything for Pavement Distress—0
Evaluation Study on SAM 2 for Class-agnostic Instance-level SegmentationCode1
Image-to-Lidar Relational Distillation for Autonomous Driving Data—0
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A SurveyCode5
SAM-SP: Self-Prompting Makes SAM Great Again—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical ImagesCode1
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
Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2Code3
X-Recon: Learning-based Patient-specific High-Resolution CT Reconstruction from Orthogonal X-Ray ImagesCode0
MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture SynthesisCode1
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentationCode2
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
RobustSAM: Segment Anything Robustly on Degraded ImagesCode3
Interpreting the Second-Order Effects of Neurons in CLIP—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