115 citations · 222 across the 12 of their papers we have counts for
12 papers
MLMT-CNN for Object Detection and Segmentation in Multi-layer and Multi-spectral Images
Majedaldein Almahasneh, Adeline Paiement, Xianghua Xie +1
Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A…
Panoptic Segmentation of Galactic Structures in LSB Images
Felix Richards, Adeline Paiement, Xianghua Xie +2
We explore the use of deep learning to localise galactic structures in low surface brightness (LSB) images. LSB imaging reveals many interesting structures, though these are freque…
Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models
Alex Milne, Xianghua Xie
Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production st…
Gradient Leakage Defense with Key-Lock Module for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local mo…
Predicting Surface Texture in Steel Manufacturing at Speed
Alexander J. M. Milne, Xianghua Xie
Control of the surface texture of steel strip during the galvanizing and temper rolling processes is essential to satisfy customer requirements and is conventionally measured post-…
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic…
Learnable Gabor modulated complex-valued networks for orientation robustness
Felix Richards, Adeline Paiement, Xianghua Xie +2
Robustness to transformation is desirable in many computer vision tasks, given that input data often exhibits pose variance. While translation invariance and equivariance is a docu…
FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition
Hanchi Ren, Jingjing Deng, Xianghua Xie +2
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, pri…
A Deep Learning Driven Active Framework for Segmentation of Large 3D Shape Collections
David George, Xianguha Xie, Yu-Kun Lai +1
High-level shape understanding and technique evaluation on large repositories of 3D shapes often benefit from additional information known about the shapes. One example of such inf…
3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks
David George, Xianghua Xie, Gary KL Tam
There is an increasing interest in applying deep learning to 3D mesh segmentation. We observe that 1) existing feature-based techniques are often slow or sensitive to feature resiz…
Graph Based Convolutional Neural Network
Michael Edwards, Xianghua Xie
The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the ima…
From Pose to Activity: Surveying Datasets and Introducing CONVERSE
Michael Edwards, Jingjing Deng, Xianghua Xie
We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent pro…