Outputs (32)

“Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving (2021)
Conference Proceeding
Stelling, J., & Atapour-Abarghouei, A. (2021). “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving. . https://doi.org/10.1109/bigdata52589.2021.9672033

Biases can filter into AI technology without our knowledge. Oftentimes, seminal deep learning networks champion increased accuracy above all else. In this paper, we attempt to alleviate biases encountered by semantic segmentation models in urban driv... Read More about “Just Drive”: Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving.

Transforming Fake News: Robust Generalisable News Classification Using Transformers (2021)
Conference Proceeding
Blackledge, C., & Atapour-Abarghouei, A. (2021). Transforming Fake News: Robust Generalisable News Classification Using Transformers. . https://doi.org/10.1109/bigdata52589.2021.9671970

As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which tra... Read More about Transforming Fake News: Robust Generalisable News Classification Using Transformers.

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing (2021)
Conference Proceeding
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. . https://doi.org/10.1109/bigdata52589.2021.9671386

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. With such a powerful solution, it is often temptin... Read More about Rank over Class: The Untapped Potential of Ranking in Natural Language Processing.

Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking (2021)
Conference Proceeding
Carrell, S., & Atapour-Abarghouei, A. (2021). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. . https://doi.org/10.1109/bigdata52589.2021.9671378

The use of mobiles phones when driving has been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance... Read More about Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking.

On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures (2021)
Conference Proceeding
Poyser, M., Atapour-Abarghouei, A., & Breckon, T. (2021). On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures. . https://doi.org/10.1109/icpr48806.2021.9412455

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. Whilst the reported performance of these a... Read More about On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures.

Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions (2019)
Conference Proceeding
Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., …Obara, B. (2019). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. In 2019 IEEE International Conference on Big Data (Big Data) (5336-5345). https://doi.org/10.1109/bigdata47090.2019.9005545

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently di... Read More about Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.

A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation (2019)
Conference Proceeding
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2019). A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation. In Proceedings of 2019 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/bigdata47090.2019.9005540

Volenti non fit injuria: Ransomware and its Victims (2019)
Conference Proceeding
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2019). Volenti non fit injuria: Ransomware and its Victims. . https://doi.org/10.1109/bigdata47090.2019.9006298

With the recent growth in the number of malicious activities on the internet, cybersecurity research has seen a boost in the past few years. However, as certain variants of malware can provide highly lucrative opportunities for bad actors, significan... Read More about Volenti non fit injuria: Ransomware and its Victims.

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019)
Conference Proceeding
Akcay, A., Atapour-Abarghouei, A., & Breckon, T. P. (2019). Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.2019.8851808

Despite inherent ill-definition, anomaly detection is a research endeavour of great interest within machine learning and visual scene understanding alike. Most commonly, anomaly detection is considered as the detection of outliers within a given data... Read More about Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection.

Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior (2019)
Conference Proceeding
Atapour-Abarghouei, A., & Breckon, T. (2019). Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior. In 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings (4295-4299). https://doi.org/10.1109/icip.2019.8803551

Monocular depth estimation using novel learning-based approaches has recently emerged as a promising potential alternative to more conventional 3D scene capture technologies within real-world scenarios. Many such solutions often depend on large quant... Read More about Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior.