SOTAVerified

Zero Shot Segmentation

Papers

Showing 125 of 134 papers

TitleStatusHype
Compress Any Segment Anything Model (SAM)Code1
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data0
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models0
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 Classification0
AI-Driven Segmentation and Analysis of Microbial Cells0
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 Data0
Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images0
SmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images0
Eye on the Target: Eye Tracking Meets Rodent Tracking0
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing0
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model0
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive ReinforcementCode4
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models0
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation0
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 Decomposition0
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI0
3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object RearrangementCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Grounded HQ-SAMMean AP49.6Unverified
2Grounded-SAMMean AP46Unverified
3UNINEXTMean AP42.1Unverified
4HIPIEMean AP41.6Unverified
5SANMean AP41.4Unverified
6odiseMean AP38.7Unverified
7OpenSEEDMean AP36.1Unverified
8OpenSDMean AP35.8Unverified
9SGinW_Team (X-Decoder-L)Mean AP32.2Unverified
10SGinW_Team (X-Decoder-B)Mean AP27.7Unverified
#ModelMetricClaimedVerifiedStatus
1COSMOS ViT-B/16mIoU17.7Unverified
2GEM (MetaCLIP)mIoU17.1Unverified
3GEM (CLIP)mIoU15.7Unverified
4CLIPSurgerymIoU12.9Unverified
5MaskCLIPmIoU10.2Unverified