1 code implementation • 22 Feb 2024 • Shuang Chen, Amir Atapour-Abarghouei, Hubert P. H. Shum
In this paper, we propose an end-to-end High-quality INpainting Transformer, abbreviated as HINT, which consists of a novel mask-aware pixel-shuffle downsampling module (MPD) to preserve the visible information extracted from the corrupted image while maintaining the integrity of the information available for high-level inferences made within the model.
no code implementations • 17 May 2023 • Shuang Chen, Amir Atapour-Abarghouei, Edmond S. L. Ho, Hubert P. H. Shum
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.
no code implementations • 27 Feb 2023 • Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough
Existing approaches to understanding the performance of hardware largely focus around benchmarking -- leveraging standardised workloads which seek to be representative of an end-user's needs.
no code implementations • 12 Dec 2022 • Michael Luke Battle, Amir Atapour-Abarghouei, Andrew Stephen McGough
Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes.
1 code implementation • 1 Aug 2022 • Shuang Chen, Amir Atapour-Abarghouei, Jane Kerby, Edmond S. L. Ho, David C. G. Sainsbury, Sophie Butterworth, Hubert P. H. Shum
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist.
1 code implementation • 11 Jul 2022 • David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough
It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance.
2 code implementations • 6 Feb 2022 • Peter J. Bevan, Amir Atapour-Abarghouei
Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment.
1 code implementation • 2 Dec 2021 • Jack Stelling, Amir Atapour-Abarghouei
However, the strategy has never been applied to the safety-critical domain of pixel-wise semantic segmentation of highly variable training data - such as urban scenes.
1 code implementation • 20 Sep 2021 • Ciara Blackledge, Amir Atapour-Abarghouei
Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F1 scores of up to 4. 9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10. 1% following the implementation of our two-step classification pipeline.
1 code implementation • 20 Sep 2021 • Peter J. Bevan, Amir Atapour-Abarghouei
Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible.
1 code implementation • 5 Sep 2021 • Steven Carrell, Amir Atapour-Abarghouei
The use of mobiles phones when driving have been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task.
no code implementations • 23 Oct 2020 • John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough
With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention.
1 code implementation • 10 Sep 2020 • Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection.
no code implementations • 28 Jul 2020 • Matt Poyser, Amir Atapour-Abarghouei, Toby P. Breckon
Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks.
1 code implementation • 16 Jul 2020 • Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason D. Connolly, Toby P. Breckon
Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network.
Signal Processing Image and Video Processing
no code implementations • 11 Dec 2019 • Bruna G. Maciel-Pearson, Letizia Marchegiani, Samet Akcay, Amir Atapour-Abarghouei, James Garforth, Toby P. Breckon
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet unsolved challenge.
1 code implementation • 21 Aug 2019 • Stephen Bonner, Amir Atapour-Abarghouei, Philip T. Jackson, John Brennan, Ibad Kureshi, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara
Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines.
Social and Information Networks
1 code implementation • 19 Aug 2019 • Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
In this paper, we investigate the possibility of classifying the ransomware a system is infected with simply based on a screenshot of the splash screen or the ransom note captured using a consumer camera commonly found in any modern mobile device.
no code implementations • 15 Aug 2019 • Amir Atapour-Abarghouei, Toby P. Breckon
Robust three-dimensional scene understanding is now an ever-growing area of research highly relevant in many real-world applications such as autonomous driving and robotic navigation.
2 code implementations • 18 Jul 2019 • Bruna G. Maciel-Pearson, Samet Akcay, Amir Atapour-Abarghouei, Christopher Holder, Toby P. Breckon
Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications.
Ranked #1 on Autonomous Flight (Dense Forest) on mtrl-auto-uav
1 code implementation • CVPR 2019 • Amir Atapour-Abarghouei, Toby P. Breckon
Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation.
1 code implementation • 26 Mar 2019 • Amir Atapour-Abarghouei, Toby P. Breckon
Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation.
Ranked #67 on Monocular Depth Estimation on KITTI Eigen split
2 code implementations • 25 Jan 2019 • Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
By contrast, we introduce an unsupervised anomaly detection model, trained only on the normal (non-anomalous, plentiful) samples in order to learn the normality distribution of the domain and hence detect abnormality based on deviation from this model.
1 code implementation • 15 Jan 2019 • Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason Connolly, Noura Al Moubayed, Toby Breckon
Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments.
Quantitative Methods Signal Processing
1 code implementation • 14 Sep 2018 • Philip T. Jackson, Amir Atapour-Abarghouei, Stephen Bonner, Toby Breckon, Boguslaw Obara
In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks.
1 code implementation • CVPR 2018 • Amir Atapour-Abarghouei, Toby P. Breckon
Monocular depth estimation using learning-based approaches has become promising in recent years.
9 code implementations • 17 May 2018 • Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
Generative Adversarial Network Semi-supervised Anomaly Detection +1
1 code implementation • 4 Sep 2017 • Amir Atapour-Abarghouei, Toby P Breckon
We address plausible hole filling in depth images in a computationally lightweight methodology that leverages recent advances in semantic scene segmentation.