Personalized Segmentation
6 papers with code • 1 benchmarks • 1 datasets
Given a one-shot image with a reference mask, the models are required to segment the indicated target object in any other images.
PerSeg
Most implemented papers
SegGPT: Segmenting Everything In Context
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
Visual 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?
Images Speak in Images: A Generalist Painter for In-Context Visual Learning
baaivision/painter
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CVPR 2023
In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.
Personalize Segment Anything Model with One Shot
zrrskywalker/personalize-sam
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Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.
Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation
Zch0414/P2SAM
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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 availability of annotated data for each patient.
Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval
dvirsamuel/PDM
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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.
