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Zero Shot Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Foundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models
Zero-Shot Tree Detection and Segmentation from Aerial Forest Imagery
Removing Watermarks with Partial Regeneration using Semantic Information
Adapting a Segmentation Foundation Model for Medical Image Classification
AI-Driven Segmentation and Analysis of Microbial Cells
3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images
SmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images
Eye on the Target: Eye Tracking Meets Rodent Tracking
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing
SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation
CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
HOLa: HoloLens Object Labeling
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
Segment to Recognize Robustly -- Enhancing Recognition by Image Decomposition
Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI
3DGS-CD: 3D Gaussian Splatting-based Change Detection for Physical Object Rearrangement
ZIM: Zero-Shot Image Matting for Anything
Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images
Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations
MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation
Prompt-and-Transfer: Dynamic Class-aware Enhancement for Few-shot Segmentation
PaveSAM Segment Anything for Pavement Distress
Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation
Image-to-Lidar Relational Distillation for Autonomous Driving Data
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey
SAM-SP: Self-Prompting Makes SAM Great Again
SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
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