Outputs (663)

Dynamic Unary Convolution in Transformers (2023)
Journal Article
Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023). Dynamic Unary Convolution in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12747 - 12759. https://doi.org/10.1109/tpami.2022.3233482

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main cont... Read More about Dynamic Unary Convolution in Transformers.

Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data (2022)
Journal Article
Watson, M., Awwad Shekh Hasan, B., & Al Moubayed, N. (2022). Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data. Scientific Reports, 12(19899), Article 19899. https://doi.org/10.1038/s41598-022-24356-6

It has been shown that identical Deep Learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and... Read More about Using Model Explanations to Guide Deep Learning Models Towards Consistent Explanations for EHR Data.

A directed graph convolutional neural network for edge-structured signals in link-fault detection (2021)
Journal Article
Kenning, M., Deng, J., Edwards, M., & Xie, X. (2022). A directed graph convolutional neural network for edge-structured signals in link-fault detection. Pattern Recognition Letters, 153, 100-106. https://doi.org/10.1016/j.patrec.2021.12.003

The growing interest in graph deep learning has led to a surge of research focusing on learning various characteristics of graph-structured data. Directed graphs have generally been treated as incidental to definitions on the more general class of un... Read More about A directed graph convolutional neural network for edge-structured signals in link-fault detection.

TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain (2021)
Journal Article
Wang, Y., Xia, Z., Deng, J., Xie, X., Gong, M., & Ma, X. (2021). TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain. BMC Bioinformatics, 22(9), 1-15. https://doi.org/10.1186/s12859-021-04190-9

From pose to activity: Surveying datasets and introducing CONVERSE (2016)
Journal Article
Edwards, M., Deng, J., & Xie, X. (2016). From pose to activity: Surveying datasets and introducing CONVERSE. Computer Vision and Image Understanding, 144, 73-105. https://doi.org/10.1016/j.cviu.2015.10.010

Joint multi-label learning and feature extraction for temporal link prediction (2022)
Journal Article
Ma, X., Tan, S., Xie, X., Zhong, X., & Deng, J. (2022). Joint multi-label learning and feature extraction for temporal link prediction. Pattern Recognition, 121, https://doi.org/10.1016/j.patcog.2021.108216

A bag of words approach to subject specific 3D human pose interaction classification with random decision forests (2014)
Journal Article
Deng, J., Xie, X., & Daubney, B. (2014). A bag of words approach to subject specific 3D human pose interaction classification with random decision forests. Graphical Models, 76(3), 162-171. https://doi.org/10.1016/j.gmod.2013.10.006

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR) (2018)
Journal Article
Boileau, E., Pant, S., Roobottom, C., Sazonov, I., Deng, J., Xie, X., & Nithiarasu, P. (2018). Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR). International Journal for Numerical Methods in Biomedical Engineering, 34(1), https://doi.org/10.1002/cnm.2908

Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions (2016)
Journal Article
Daubney, B., Xie, X., Deng, J., Mac Parthaláin, N., & Zwiggelaar, R. (2016). Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions. Image and Vision Computing, 52, 73-87. https://doi.org/10.1016/j.imavis.2016.05.010

3D Interactive Segmentation With Semi-Implicit Representation and Active Learning (2021)
Journal Article
Deng, J., & Xie, X. (2021). 3D Interactive Segmentation With Semi-Implicit Representation and Active Learning. IEEE Transactions on Image Processing, 30, 9402-9417. https://doi.org/10.1109/tip.2021.3125491

Distillation of human–object interaction contexts for action recognition (2022)
Journal Article
Almushyti, M., & Li, F. W. (2022). Distillation of human–object interaction contexts for action recognition. Computer Animation and Virtual Worlds, 33(5), Article e2107. https://doi.org/10.1002/cav.2107

Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus... Read More about Distillation of human–object interaction contexts for action recognition.

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment (2022)
Journal Article
Chai, R., Niu, H., Carrasco, J., Arvin, F., Yin, H., & Lennox, B. (2022). Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/tnnls.2022.3209154

Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles using Spring Damping Energy Model (2022)
Journal Article
Xie, S., Hu, J., Ding, Z., & Arvin, F. (2023). Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles using Spring Damping Energy Model. IEEE Transactions on Vehicular Technology, 72(3), 2974 - 2987. https://doi.org/10.1109/tvt.2022.3218575

Sampling strategies for learning-based 3D medical image compression (2022)
Journal Article
Nagoor, O. H., Whittle, J., Deng, J., Mora, B., & Jones, M. W. (2022). Sampling strategies for learning-based 3D medical image compression. https://doi.org/10.1016/j.mlwa.2022.100273

GRNN: generative regression neural network—a data leakage attack for federated learning (2022)
Journal Article
Ren, H., Deng, J., & Xie, X. (2022). GRNN: generative regression neural network—a data leakage attack for federated learning. ACM Transactions on Intelligent Systems and Technology, 13(4), 1-24. https://doi.org/10.1145/3510032

An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012)
Journal Article
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012). An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation. IEEE Transactions on Biomedical Engineering, 59(9), 2538-2548. https://doi.org/10.1109/tbme.2012.2205687

EfficientTDNN: Efficient Architecture Search for Speaker Recognition (2022)
Journal Article
Wang, R., Wei, Z., Duan, H., Ji, S., Long, Y., & Hong, Z. (2022). EfficientTDNN: Efficient Architecture Search for Speaker Recognition. IEEE/ACM Transactions on Audio, Speech and Language Processing, 30, 2267-2279. https://doi.org/10.1109/taslp.2022.3182856

Convolutional neural networks (CNNs), such as the time-delay neural network (TDNN), have shown their remarkable capability in learning speaker embedding. However, they meanwhile bring a huge computational cost in storage size, processing, and memory.... Read More about EfficientTDNN: Efficient Architecture Search for Speaker Recognition.

Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes (2013)
Journal Article
Thida, M., Eng, H., & Remagnino, P. (2013). Laplacian Eigenmap With Temporal Constraints for Local Abnormality Detection in Crowded Scenes. IEEE Transactions on Cybernetics, 43(6), 2147-2156. https://doi.org/10.1109/tcyb.2013.2242059

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer (2020)
Journal Article
Welikala, R. A., Remagnino, P., Lim, J. H., Chan, C. S., Rajendran, S., Kallarakkal, T. G., …Barman, S. A. (2020). Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer. IEEE Access, 8, 132677-132693. https://doi.org/10.1109/access.2020.3010180

The boundary node method for multi-robot multi-goal path planning problems (2021)
Journal Article
Saeed, R. A., Recupero, D. R., & Remagnino, P. (2021). The boundary node method for multi-robot multi-goal path planning problems. Expert Systems, 38(6), Article e12691. https://doi.org/10.1111/exsy.12691