Papers Zero Shot Segmentation
“Zero Shot Segmentation” 태그가 달린 논문 135편 · 필터 해제
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation. The Segment Anything Model (SAM), in particular, offers powerful zero shot seg…
Zero Shot SegmentationCompress Any Segment Anything Model (SAM)
Due to the excellent performance in yielding high-quality, zero-shot segmentation, Segment Anything Model (SAM) and its variants have been widely applied in diverse scenarios such as healthcare and intelligent manufactur…
modelQuantizationZero Shot SegmentationFoundation Models for Zero-Shot Segmentation of Scientific Images without AI-Ready Data
Zero-shot and prompt-based technologies capitalized on using frequently occurring images to transform visual reasoning tasks, which explains why such technologies struggle with valuable yet scarce scientific image sets. …
Visual ReasoningZero Shot SegmentationMRI-CORE: A Foundation Model for Magnetic Resonance Imaging
The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or…
Diagnosticimage-classificationImage ClassificationSegmentation+2Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models
Automated sorting is crucial for improving the efficiency and scalability of textile recycling, but accurately identifying material composition and detecting contaminants from sensor data remains challenging. This paper …
SegmentationTransfer LearningZero Shot SegmentationZero-Shot Tree Detection and Segmentation from Aerial Forest Imagery
Large-scale delineation of individual trees from remote sensing imagery is crucial to the advancement of ecological research, particularly as climate change and other environmental factors rapidly transform forest landsc…
Image SegmentationSegmentationSemantic SegmentationZero Shot SegmentationRemoving Watermarks with Partial Regeneration using Semantic Information
As AI-generated imagery becomes ubiquitous, invisible watermarks have emerged as a primary line of defense for copyright and provenance. The newest watermarking schemes embed semantic signals - content-aware patterns tha…
SSIMZero Shot SegmentationAdapting a Segmentation Foundation Model for Medical Image Classification
Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to their impressive zero-shot segmentation c…
Classificationimage-classificationImage ClassificationImage Segmentation+4AI-Driven Segmentation and Analysis of Microbial Cells
Studying the growth and metabolism of microbes provides critical insights into their evolutionary adaptations to harsh environments, which are essential for microbial research and biotechnology applications. In this stud…
DenoisingSegmentationZero Shot Segmentation3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
Existing zero-shot 3D point cloud segmentation methods often struggle with limited transferability from seen classes to unseen classes and from semantic to visual space. To alleviate this, we introduce 3D-PointZshotS, a …
Point Cloud SegmentationSemantic SegmentationTransfer LearningZero Shot Segmentation+1SynthFM: Training Modality-agnostic Foundation Models for Medical Image Segmentation without Real Medical Data
Foundation models like the Segment Anything Model (SAM) excel in zero-shot segmentation for natural images but struggle with medical image segmentation due to differences in texture, contrast, and noise. Annotating medic…
DecoderImage SegmentationMedical Image SegmentationSemantic Segmentation+2Resilience of Vision Transformers for Domain Generalisation in the Presence of Out-of-Distribution Noisy Images
Modern AI models excel in controlled settings but often fail in real-world scenarios where data distributions shift unpredictably - a challenge known as domain generalisation (DG). This paper tackles this limitation by r…
Zero Shot SegmentationSmartScan: An AI-based Interactive Framework for Automated Region Extraction from Satellite Images
The deployment of a continuous methane monitoring system requires determining the optimal number and placement of fixed sensors. However, planning is labor-intensive, requiring extensive site setup and iteration to meet …
Zero Shot SegmentationEye on the Target: Eye Tracking Meets Rodent Tracking
Analyzing animal behavior from video recordings is crucial for scientific research, yet manual annotation remains labor-intensive and prone to subjectivity. Efficient segmentation methods are needed to automate this proc…
SegmentationZero Shot SegmentationVisual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing
Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite thei…
Image SegmentationSegmentationSemantic SegmentationZero Shot SegmentationSeg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To addres…
Domain GeneralizationObject DetectionOpen Vocabulary Object DetectionOpen Vocabulary Semantic Segmentation+5SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model
Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promis…
Instance SegmentationQuantizationSemantic SegmentationZero Shot SegmentationShow and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
Modern deep neural networks have now reached human-level performance across a variety of tasks. However, unlike humans they lack the ability to explain their decisions by showing where and telling what concepts guided th…
Zero Shot SegmentationFew-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation
Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models like the Segment Anything Model (SAM) enable generalizable zero-shot segme…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1CellViT++: Energy-Efficient and Adaptive Cell Segmentation and Classification Using Foundation Models
Digital Pathology is a cornerstone in the diagnosis and treatment of diseases. A key task in this field is the identification and segmentation of cells in hematoxylin and eosin-stained images. Existing methods for cell s…
Cell SegmentationDataset GenerationSegmentationZero Shot Segmentation