Learning discriminatory deep clustering models
(2019)
Conference Proceeding
Alqahtani, A., Xie, X., Deng, J., & Jones, M. W. (2019). Learning discriminatory deep clustering models. . https://doi.org/10.1007/978-3-030-29888-3_18
Urban Scene Segmentation using Semi-supervised GAN (2019)
Conference Proceeding
Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Urban Scene Segmentation using Semi-supervised GAN. In L. Bruzzone, F. Bovolo, & J. Benediktsson (Eds.), . https://doi.org/10.1117/12.2533055
Smart Monitoring of Crops Using Generative Adversarial Networks (2019)
Conference Proceeding
Kerdegari, H., Razaak, M., Argyriou, V., & Remagnino, P. (2019). Smart Monitoring of Crops Using Generative Adversarial Networks. In M. Vento, & G. Percannella (Eds.), . https://doi.org/10.1007/978-3-030-29888-3%5C_45
Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images (2019)
Conference Proceeding
Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019). Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images. In D. Tzovaras, D. Giakoumis, M. Vincze, & A. Argyros (Eds.), . https://doi.org/10.1007/978-3-030-34995-0%5C_71
An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies (2019)
Conference Proceeding
Razaak, M., Kerdegari, H., Davies, E., Abozariba, R., Broadbent, M., Mason, K., …Remagnino, P. (2019). An Integrated Precision Farming Application Based on 5G, UAV and Deep Learning Technologies. In M. Vento, & G. Percannella (Eds.), . https://doi.org/10.1007/978-3-030-29930-9%5C_11
A Comparison of Embedded Deep Learning Methods for Person Detection (2019)
Conference Proceeding
Kim, C. E., Oghaz, M. M. D., Fajtl, J., Argyriou, V., & Remagnino, P. (2019). A Comparison of Embedded Deep Learning Methods for Person Detection. In A. Tremeau, G. Farinella, & J. Braz (Eds.), . https://doi.org/10.5220/0007386304590465
Colour Processing in Adversarial Attacks on Face Liveness Systems (2019)
Conference Proceeding
Abduh, L., & Ivrissimtzis, I. (2019). Colour Processing in Adversarial Attacks on Face Liveness Systems. In F. P. Vidal, G. K. . L. Tam, & J. C. Roberts (Eds.), Proceedings of Computer Graphics and Visual Computing 2019 (CGVC) (149-152). https://doi.org/10.2312/cgvc.20191272In the context of face recognition systems, liveness test is a binary classification task aiming at distinguishing between input images that come from real people’s faces and input images that come from photos or videos of those faces, and presented... Read More about Colour Processing in Adversarial Attacks on Face Liveness Systems.
Order Matters: Shuffling Sequence Generation for Video Prediction (2019)
Conference Proceeding
Wang, J., Hu, B., Long, Y., & Guan, Y. (2019). Order Matters: Shuffling Sequence Generation for Video Prediction.
On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding (2019)
Conference Proceeding
Ismail, K., & Breckon, T. (2019). On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (641-646). https://doi.org/10.1109/icmla.2019.00117Whilst real-time object detection has become an increasingly important task within urban scene understanding for autonomous driving, the majority of prior work concentrates on the detection of obstacles, dynamic scene objects (pedestrians, vehicles)... Read More about On the Performance of Extended Real-Time Object Detection and Attribute Estimation within Urban Scene Understanding.
On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery (2019)
Conference Proceeding
Bhowmik, N., Gaus, Y., Akcay, S., Barker, J., & Breckon, T. (2019). On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (986-991). https://doi.org/10.1109/icmla.2019.00168X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anom... Read More about On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery.
Region Based Anomaly Detection With Real-Time Training and Analysis (2019)
Conference Proceeding
Adey, P., Bordewich, M., Breckon, T., & Hamilton, O. (2019). Region Based Anomaly Detection With Real-Time Training and Analysis. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (495-499). https://doi.org/10.1109/icmla.2019.00092We present a method of anomaly detection that is capable of real-time operation on a live stream of images. The real-time performance applies to the training of the algorithm as well as subsequent analysis, and is achieved by substituting the region... Read More about Region Based Anomaly Detection With Real-Time Training and Analysis.
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation (2019)
Conference Proceeding
Rimboux, A., Dupre, R., Daci, E., Lagkas, T., Sarigiannidis, P., Remagnino, P., & Argyriou, V. (2019). Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation. . https://doi.org/10.1109/dcoss.2019.00070
Scene and Environment Monitoring Using Aerial Imagery and Deep Learning (2019)
Conference Proceeding
Oghaz, M. M., Razaak, M., Kerdegari, H., Argyriou, V., & Remagnino, P. (2019). Scene and Environment Monitoring Using Aerial Imagery and Deep Learning. . https://doi.org/10.1109/dcoss.2019.00078
Latent Bernoulli Autoencoder (2019)
Conference Proceeding
Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2019). Latent Bernoulli Autoencoder.
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/bigdata47090.2019.9006298With 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.
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/bigdata47090.2019.9005545Graphs 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 General Transductive Regularizer for Zero-Shot Learning (2019)
Conference Proceeding
Mao, H., Zhang, H., Long, Y., Wang, S., & Yang, L. (2019). A General Transductive Regularizer for Zero-Shot Learning.
Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery (2019)
Conference Proceeding
Gaus, Y., Bhowmik, N., Akcay, S., & Breckon, T. (2019). Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019 (420-425). https://doi.org/10.1109/icmla.2019.00079X-ray imagery security screening is essential to maintaining transport security against a varying profile of threat or prohibited items. Particular interest lies in the automatic detection and classification of weapons such as firearms and knives wit... Read More about Evaluating the Transferability and Adversarial Discrimination of Convolutional Neural Networks for Threat Object Detection and Classification within X-Ray Security Imagery.
Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items (2019)
Conference Proceeding
Bhowmik, N., Gaus, Y., & Breckon, T. (2019). Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items. In Proceeding of the International Symposium on Technologies for Homeland Security (1-6). https://doi.org/10.1109/hst47167.2019.9032920X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of part... Read More about Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items.
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