One-Shot Segmentation
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Benchmarks
Cluttered Omniglot
Most implemented
One-Shot Learning for Semantic Segmentation
Image Segmentation Using Text and Image Prompts
One Shot is Enough for Sequential Infrared Small Target Segmentation
Deep ContourFlow: Advancing Active Contours with Deep Learning
Papers
RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation
Training-free one-shot segmentation offers a scalable alternative to expert annotations where knowledge is often transferred from support images and foundation models. But existing methods often treat all pixels in suppo…
One-Shot SegmentationPolyp SegmentationMatch4Annotate: Propagating Sparse Video Annotations via Implicit Neural Feature Matching
Acquiring per-frame video annotations remains a primary bottleneck for deploying computer vision in specialized domains such as medical imaging, where expert labeling is slow and costly. Label propagation offers a natura…
One-Shot SegmentationAn Efficient Model-Driven Groupwise Approach for Atlas Construction
Atlas construction is fundamental to medical image analysis, offering a standardized spatial reference for tasks such as population-level anatomical modeling. While data-driven registration methods have recently shown pr…
One-Shot SegmentationDC-SAM: In-Context Segment Anything in Images and Videos via Dual Consistency
Given a single labeled example, in-context segmentation aims to segment corresponding objects. This setting, known as one-shot segmentation in few-shot learning, explores the segmentation model's generalization ability a…
Few-Shot LearningInteractive SegmentationOne-Shot SegmentationScene Understanding+6Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
Decentralized federated learning enables learning of data representations from multiple sources without compromising the privacy of the clients. In applications like medical image segmentation, where obtaining a large an…
Federated LearningImage SegmentationMedical Image SegmentationOne-Shot Segmentation+3Data Adaptive Few-shot Multi Label Segmentation with Foundation Model
The high cost of obtaining accurate annotations for image segmentation and localization makes the use of one and few shot algorithms attractive. Several state-of-the-art methods for few-shot segmentation have emerged, in…
Image SegmentationOne-Shot SegmentationSegmentationSemantic Segmentation