C2SPoint: A classification-to-saliency network for point cloud saliency detection (2023)
Journal Article
Jiang, Z., Ding, L., Tam, G., Song, C., Li, F. W., & Yang, B. (2023). C2SPoint: A classification-to-saliency network for point cloud saliency detection. Computers and Graphics, https://doi.org/10.1016/j.cag.2023.07.003

Point cloud saliency detection is an important technique that support downstream tasks in 3D graphics and vision, like 3D model simplification, compression, reconstruction and viewpoint selection. Existing approaches often rely on hand-crafted featur... Read More about C2SPoint: A classification-to-saliency network for point cloud saliency detection.

OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality (2023)
Journal Article
Sibrina, D., Bethapudi, S., & Koulieris, G. A. (2023). OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality. Visual Computer, https://doi.org/10.1007/s00371-023-02949-0

Rotational abnormalities in the lower limbs causing patellar mal-tracking negatively affect patients’ lives, particularly young patients (10–17 years old). Recent studies suggest that rotational abnormalities can increase degenerative effects on the... Read More about OrthopedVR: clinical assessment and pre-operative planning of paediatric patients with lower limb rotational abnormalities in virtual reality.

Bivariate non-uniform subdivision schemes based on L-systems (2023)
Journal Article
Gérot, C., & Ivrissimtzis, I. (2023). Bivariate non-uniform subdivision schemes based on L-systems. Applied Mathematics and Computation, 457, Article 128156. https://doi.org/10.1016/j.amc.2023.128156

L–systems have been used to describe non-uniform, univariate, subdivision schemes, which offer more flexible refinement processes than the uniform schemes, while at the same time are easier to analyse than the geometry driven non-uniform schemes. In... Read More about Bivariate non-uniform subdivision schemes based on L-systems.

Hierarchical Graph Convolutional Networks for Action Quality Assessment (2023)
Journal Article
Zhou, K., Ma, Y., Shum, H. P., & Liang, X. (2023). Hierarchical Graph Convolutional Networks for Action Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology, https://doi.org/10.1109/TCSVT.2023.3281413

Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing work... Read More about Hierarchical Graph Convolutional Networks for Action Quality Assessment.

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network (2023)
Journal Article
Chen, S., Atapour-Abarghouei, A., Ho, E. S., & Shum, H. P. (2023). INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network. Software impacts, 17, Article 100517. https://doi.org/10.1016/j.simpa.2023.100517

We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients’ privacy, we design a software framework using image... Read More about INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network.

Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data (2023)
Journal Article
Kluvanec, D., McCaffrey, K. J., Phillips, T. B., & Al Moubayed, N. (2023). Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data. IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/tgrs.2023.3273329

We can represent the orientation of a plane in 3D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3D that are negation invariant and can be used b... Read More about Negation Invariant Representations of 3D Vectors for Deep Learning Models applied to Fault Geometry Mapping in 3D Seismic Reflection Data.

Effect of manipulating the vergence/accommodation and image size mismatches of the ±2D flipper test on the frequency and precision of accommodative facility (2023)
Journal Article
Vera, J., Redondo, B., Martínez‐Tovar, J. M., Molina, R., Koulieris, G. A., Allen, P. M., & Jiménez, R. (2023). Effect of manipulating the vergence/accommodation and image size mismatches of the ±2D flipper test on the frequency and precision of accommodative facility. Ophthalmic and Physiological Optics, 43(4), 660-667. https://doi.org/10.1111/opo.13136

Purpose The ±2.00 D accommodative facility test presents several limitations, including the lack of objective information and inherent characteristics such as vergence/accommodative conflict, change in apparent size of the image, subjective criteria... Read More about Effect of manipulating the vergence/accommodation and image size mismatches of the ±2D flipper test on the frequency and precision of accommodative facility.

Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition (2023)
Journal Article
Men, Q., Ho, E. S., Shum, H. P., & Leung, H. (2023). Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition. Neurocomputing, 537, 198-209. https://doi.org/10.1016/j.neucom.2023.03.070

Learning view-invariant representation is a key to improving feature discrimination power for skeleton-based action recognition. Existing approaches cannot effectively remove the impact of viewpoint due to the implicit view-dependent representations.... Read More about Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition.

Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders (2023)
Journal Article
Wang, Q., & Breckon, T. (2023). Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders. Neural Networks, 163, 40-52. https://doi.org/10.1016/j.neunet.2023.03.033

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classific... Read More about Generalized Zero-Shot Domain Adaptation via Coupled Conditional Variational Autoencoders.

Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms (2023)
Journal Article
Vali, M., Mohammadi, M., Zarei, N., Samadi, M., Atapour-Abarghouei, A., Supakontanasan, W., …Fard, M. A. (2023). Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms. American Journal of Ophthalmology, 252, 1-8. https://doi.org/10.1016/j.ajo.2023.02.016

Purpose : A deep learning framework to differentiate glaucomatous optic disc changes (GON) from non-glaucomatous optic neuropathy-related disc changes (NGON). Design : Cross-sectional study. Method : A deep-learning system was trained, validated, and... Read More about Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms.

IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation (2023)
Journal Article
Yang, B., zhu, C., Li, F. W., Wei, T., Liang, X., & Wang, Q. (2023). IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation. Virtual Reality & Intelligent Hardware, 5(1), https://doi.org/10.1016/j.vrih.2022.06.006

Judging how an image is visually appealing is a complicated and subjective task. This highly motivates having a machine learning model to automatically evaluate image aesthetic by matching the aesthetics of general public. Although deep learning meth... Read More about IAACS: Image Aesthetic Assessment Through Color Composition And Space Formation.

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis (2023)
Journal Article
Zhou, K., Cai, R., Ma, Y., Tan, Q., Wang, X., Li, J., …Liang, X. (2023). A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2456-2466. https://doi.org/10.1109/tvcg.2023.3247092

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle invol... Read More about A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis.

Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation (2023)
Journal Article
Wang, Q., Meng, F., & Breckon, T. (2023). Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation. Neural Networks, 161, 614-625. https://doi.org/10.1016/j.neunet.2023.02.006

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified c... Read More about Data Augmentation with norm-VAE and Selective Pseudo-Labelling for Unsupervised Domain Adaptation.

Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements (2023)
Journal Article
Wu, K., Hu, J., Ding, Z., & Arvin, F. (2023). Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2023.3239748

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of... Read More about Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements.

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms (2023)
Journal Article
Na, S., Roucek, T., Ulrich, J., Pikman, J., Krajnik, T., Lennox, B., & Arvin, F. (2023). Federated Reinforcement Learning for Collective Navigation of Robotic Swarms. IEEE Transactions on Cognitive and Developmental Systems, https://doi.org/10.1109/tcds.2023.3239815

The recent advancement of Deep Reinforcement Learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex control... Read More about Federated Reinforcement Learning for Collective Navigation of Robotic Swarms.

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.