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Zero Shot Segmentation

Papers

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Showing 101–125 of 134 papers

TitleStatusHype
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation—0
Pedestrian Accessible Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types—0
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging—0
Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition—0
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models—0
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation—0
SILC: Improving Vision Language Pretraining with Self-Distillation—0
SmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images—0
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments—0
Exploring Open-Vocabulary Semantic Segmentation without Human Labels—0
Eye on the Target: Eye Tracking Meets Rodent Tracking—0
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
Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)Code0
PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI EstimationCode0
HOLa: HoloLens Object LabelingCode0
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic SegmentationCode0
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic DataCode0
Exploring Open-Vocabulary Semantic Segmentation from CLIP Vision Encoder Distillation OnlyCode0
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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