Papers One-Shot Segmentation
“One-Shot Segmentation” 태그가 달린 논문 38편 · 필터 해제
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 SegmentationCorrelation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images
Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks …
Few-Shot LearningImage SegmentationMedical Image SegmentationOne-Shot Learning+4One Shot is Enough for Sequential Infrared Small Target Segmentation
Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation (IRSTS) with minimal data. I…
One-Shot SegmentationSegmentationZero-shot GeneralizationDense Self-Supervised Learning for Medical Image Segmentation
Deep learning has revolutionized medical image segmentation, but it relies heavily on high-quality annotations. The time, cost and expertise required to label images at the pixel-level for each new task has slowed down w…
DecoderImage SegmentationMedical Image SegmentationMRI segmentation+4Deep ContourFlow: Advancing Active Contours with Deep Learning
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for…
Deep LearningImage SegmentationOne-Shot SegmentationSegmentation+2CycleSAM: One-Shot Surgical Scene Segmentation using Cycle-Consistent Feature Matching to Prompt SAM
The recently introduced Segment-Anything Model (SAM) has the potential to greatly accelerate the development of segmentation models. However, directly applying SAM to surgical images has key limitations including (1) the…
One-Shot SegmentationScene SegmentationSegmentationSemantic SegmentationNon-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI
Adversarial learning helps generative models translate MRI from source to target sequence when lacking paired samples. However, implementing MRI synthesis with adversarial learning in clinical settings is challenging due…
BraTS2021Contrastive LearningOne-Shot SegmentationNubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation
Driven by large data trained segmentation models, such as SAM , research in one-shot segmentation has experienced significant advancements. Recent contributions like PerSAM and MATCHER , presented at ICLR 2024, utilize a…
One-Shot SegmentationSegmentationStyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
One-shot segmentation of brain tissue requires training registration-segmentation (reg-seg) dual-model iteratively, where reg-model aims to provide pseudo masks of unlabeled images for seg-model by warping a carefully-la…
One-Shot SegmentationGroup-On: Boosting One-Shot Segmentation with Supportive Query
One-shot semantic segmentation aims to segment query images given only ONE annotated support image of the same class. This task is challenging because target objects in the support and query images can be largely differe…
One-Shot SegmentationSegmentationSemantic SegmentationDeepATLAS: One-Shot Localization for Biomedical Data
This paper introduces the DeepATLAS foundational model for localization tasks in the domain of high-dimensional biomedical data. Upon convergence of the proposed self-supervised objective, a pretrained model maps an inpu…
One-Shot SegmentationPrototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning
The potential for augmenting the segmentation of brain tumors through the use of few-shot learning is vast. Although several deep learning networks (DNNs) demonstrate promising results in terms of segmentation, they requ…
Few-Shot LearningImage SegmentationMetric LearningOne-Shot Learning+3SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target. The resulting models can be gen…
Meta-LearningOne-Shot SegmentationSegmentationSemantic Segmentation+2AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
Accurate automatic segmentation of medical images typically requires large datasets with high-quality annotations, making it less applicable in clinical settings due to limited training data. One-shot segmentation based …
AnatomyData AugmentationDiversityImage Segmentation+4One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt
Semantic segmentation of microscopic cell images using deep learning is an important technique, however, it requires a large number of images and ground truth labels for training. To address the above problem, we conside…
Image SegmentationOne-Shot SegmentationPrompt LearningSegmentation+1