One-Shot Segmentation
17 papers with code • 1 benchmarks • 3 datasets
( Image credit: One-Shot Learning for Semantic Segmentation )
Latest papers
SEGIC: 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.
AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance.
One-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.
One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation
However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts.
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN.
Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation
In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues.
One-Shot Synthesis of Images and Segmentation Masks
To this end, inspired by the recent architectural developments of single-image GANs, we introduce our OSMIS model which enables the synthesis of segmentation masks that are precisely aligned to the generated images in the one-shot regime.
One-Shot Transfer of Affordance Regions? AffCorrs!
In this work, we tackle one-shot visual search of object parts.
Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
Recent self-supervised advances in medical computer vision exploit global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation.
Image Segmentation Using Text and Image Prompts
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.