Search Results for author:
Found 6 papers, 0 papers with code
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints.
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples.
Uncertainty-Aware Semi-Supervised Few Shot Segmentation
During inference, query segmentation is predicted using prototypes from both support and unlabeled images including low-level features of the query images.
A Meta-Learning Approach for Medical Image Registration
To quickly adapt to various tasks, the meta learner was updated to get close to the center of parameters which are fine-tuned for each registration task.
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation.
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
In addition to the unified learning of feature similarity and a few shot classifier, our method leads to emphasize informative features in support data relevant to the query data, which generalizes better on unseen subjects.
