Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
A comparative review of recent few-shot object detection algorithms
L Jiaxu, C Taiyue, G Xinbo, Y Yongtao, W Ye… - arXiv preprint arXiv …, 2021 - arxiv.org
Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is
an imperative and long-lasting problem due to the inherent long-tail distribution of real-world …
an imperative and long-lasting problem due to the inherent long-tail distribution of real-world …
Few-shot semantic segmentation: a review on recent approaches
Z Chang, Y Lu, X Ran, X Gao, X Wang - Neural Computing and …, 2023 - Springer
Few-shot semantic segmentation (FSS) is a challenging task that aims to learn to segment
novel categories with only a few labeled images, and it has a wide range of real-world …
novel categories with only a few labeled images, and it has a wide range of real-world …
On the effects of randomness on stability of learning with limited labelled data: A systematic literature review
Learning with limited labelled data, such as few-shot learning, meta-learning or transfer
learning, aims to effectively train a model using only small amount of labelled samples …
learning, aims to effectively train a model using only small amount of labelled samples …
Few-shot point cloud semantic segmentation via contrastive self-supervision and multi-resolution attention
This paper presents an effective few-shot point cloud semantic segmentation approach for
real-world applications. Existing few-shot segmentation methods on point cloud heavily rely …
real-world applications. Existing few-shot segmentation methods on point cloud heavily rely …
Bi-directional feature reconstruction network for fine-grained few-shot image classification
The main challenge for fine-grained few-shot image classification is to learn feature
representations with higher inter-class and lower intra-class variations, with a mere few …
representations with higher inter-class and lower intra-class variations, with a mere few …
基于计算机视觉的工业金属表面缺陷检测综述
伍麟， 郝鸿宇， 宋友 - 自动化学报, 2023 - aas.net.cn
针对金属平面及三维结构材料的工业表面缺陷检测, 本文概述了视觉检测技术的基本原理和研究
现状, 并总结出视觉自动检测系统的关键技术包括光学成像技术, 图像预处理技术与缺陷检测器 …
现状, 并总结出视觉自动检测系统的关键技术包括光学成像技术, 图像预处理技术与缺陷检测器 …
Cardiac disease classification using two-dimensional thickness and few-shot learning based on magnetic resonance imaging image segmentation
Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the
noninvasive assessment of cardiac functions. Deep neural networks have achieved …
noninvasive assessment of cardiac functions. Deep neural networks have achieved …
Few-shot node classification on attributed networks based on deep metric learning for Cyber–Physical–Social Services
G Zhang, Y Zhao, J Wang - Pattern Recognition Letters, 2023 - Elsevier
Abstract In Cyber–Physical–Social Systems (CPSS), the interactions among various entities
form complex graphs. Many tasks can be formulated as instances of node classification …
form complex graphs. Many tasks can be formulated as instances of node classification …
Cam/cad point cloud part segmentation via few-shot learning
3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D
segmentation contributes to lower defective rate of work-pieces produced by the …
segmentation contributes to lower defective rate of work-pieces produced by the …