Personalized Segmentation
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Benchmarks
PerSeg
Most implemented
SegGPT: Segmenting Everything In Context
Personalize Segment Anything Model with One Shot
Images Speak in Images: A Generalist Painter for In-Context Visual Learning
Visual Prompting via Image Inpainting
Papers
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 Segmentation