Papers Personalized Segmentation
“Personalized Segmentation” 태그가 달린 논문 13편 · 필터 해제
Attention-Based Prototype Calibration for Multi-Rater Few-Shot Medical Image Segmentation
Few-shot medical image segmentation methods typically assume a single ground-truth annotation, overlooking systematic variability across expert raters commonly observed in clinical datasets. We propose an attention-based…
Medical Image SegmentationPersonalized SegmentationINSID3: Training-Free In-Context Segmentation with DINOv3
In-context segmentation (ICS) aims to segment arbitrary concepts, e.g., objects, parts, or personalized instances, given one annotated visual examples. Existing work relies on (i) fine-tuning vision foundation models (VF…
Personalized SegmentationSemantic correspondenceRetrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?
Open-vocabulary segmentation (OVS) extends the zero-shot recognition capabilities of vision-language models (VLMs) to pixel-level prediction, enabling segmentation of arbitrary categories specified by text prompts. Despi…
Personalized SegmentationTwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Brain tumor segmentation is critical in diagnosis and treatment planning for the disease. Yet, current deep learning methods rely on centralized data collection, which raises privacy concerns and limits generalization ac…
Personalized SegmentationBrain Tumor SegmentationFederated LearningPersonalized Federated Segmentation with Shared Feature Aggregation and Boundary-Focused Calibration
Personalized federated learning (PFL) possesses the unique capability of preserving data confidentiality among clients while tackling the data heterogeneity problem of non-independent and identically distributed (Non-IID…
Personalized Federated LearningMedical Image SegmentationPersonalized SegmentationTumor SegmentationTowards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-s…
Personalized SegmentationInstance SegmentationConditional Conformal Risk Adaptation
Uncertainty quantification is becoming increasingly important in image segmentation, especially for high-stakes applications like medical imaging. While conformal risk control generalizes conformal prediction beyond stan…
Conformal PredictionImage SegmentationPersonalized SegmentationSegmentation+2Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval
Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervised methods are effective, they require ex…
Personalized SegmentationRetrievalSegmentationPart-aware Personalized Segment Anything Model for Patient-Specific Segmentation
Precision medicine, such as patient-adaptive treatments utilizing medical images, poses new challenges for image segmentation algorithms due to (1) the large variability across different patients and (2) the limited avai…
Patient-Specific SegmentationPersonalized SegmentationPersonalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific …
Image GenerationmodelPersonalized SegmentationSegmentation+5SegGPT: Segmenting Everything In Context
We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by tr…
Few-Shot Semantic SegmentationIn-Context LearningPanoptic SegmentationPersonalized Segmentation+4Images Speak in Images: A Generalist Painter for In-Context Visual Learning
In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that …
In-Context LearningKeypoint DetectionPersonalized SegmentationSemantic SegmentationVisual Prompting via Image Inpainting
How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-outp…
ColorizationEdge DetectionForeground SegmentationImage Inpainting+4