MetaMedSeg: volumetric meta-learning for few-shot organ segmentation
A Farshad, A Makarevich, V Belagiannis… - MICCAI Workshop on …, 2022 - Springer
The lack of sufficient annotated image data is a common issue in medical image
segmentation. For some organs and densities, the annotation may be scarce, leading to …
segmentation. For some organs and densities, the annotation may be scarce, leading to …
Unsupervised few-shot image classification by learning features into clustering space
Most few-shot image classification methods are trained based on tasks. Usually, tasks are
built on base classes with a large number of labeled images, which consumes large effort …
built on base classes with a large number of labeled images, which consumes large effort …
A fault diagnosis framework using unlabeled data based on automatic clustering with meta-learning
With the growth of the industrial internet of things, the poor performance of conventional
deep learning models hinders the application of intelligent diagnosis methods in industrial …
deep learning models hinders the application of intelligent diagnosis methods in industrial …
Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating
Z Zhao, Y Jiao, Y Xu, Z Chen, R Zhao - Advanced Engineering Informatics, 2024 - Elsevier
During operation of rotating machinery, collecting high-quality labeled fault samples is
difficult, and the corresponding data annotation is time consuming and costly. Therefore …
difficult, and the corresponding data annotation is time consuming and costly. Therefore …
[图书][B] Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XX
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …
proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel …
Metamedseg: volumetric meta-learning for few-shot organ segmentation
A Makarevich, A Farshad, V Belagiannis… - arXiv preprint arXiv …, 2021 - arxiv.org
The lack of sufficient annotated image data is a common issue in medical image
segmentation. For some organs and densities, the annotation may be scarce, leading to …
segmentation. For some organs and densities, the annotation may be scarce, leading to …
Your robot is watching 2: Using emotion features to predict the intent to deceive
V Surendran, K Mokhtari… - 2021 30th IEEE …, 2021 - ieeexplore.ieee.org
The capabilities and acceptance of social robots would be greatly improved by developing
their ability to determine human intent. Trust, which is an important consideration in …
their ability to determine human intent. Trust, which is an important consideration in …
Few-Shot Continual Learning: Approaches and Future Directions
R Lichode, S Karmore - 2023 14th International Conference on …, 2023 - ieeexplore.ieee.org
Few Shots Continual Learning (FSCL) is very challenging task, where the model has to
continual learn with very few examples. This situation happens in many scientific and …
continual learn with very few examples. This situation happens in many scientific and …
Does Catastrophic Forgetting Negatively Affect Financial Predictions?
A Zurli, A Bertugli, J Credi - … on Machine Learning, Optimization, and Data …, 2022 - Springer
Nowadays, financial markets produce a large amount of data, in the form of historical time
series, which quantitative researchers have recently attempted at predicting with deep …
series, which quantitative researchers have recently attempted at predicting with deep …
Generalising via meta-examples for continual learning in the wild
Future deep learning systems call for techniques that can deal with the evolving nature of
temporal data and scarcity of annotations when new problems occur. As a step towards this …
temporal data and scarcity of annotations when new problems occur. As a step towards this …