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Outputs (41)

Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices (2020)
Presentation / Conference Contribution
Khadka, A., Argyriou, V., & Remagnino, P. (2020, December). Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. Presented at 16TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2020) IEEE Comp Soc

SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS (2020)
Presentation / Conference Contribution
Khadka, A., Remagnino, P., & Argyriou, V. (2020, December). SYNTHETIC CROWD AND PEDESTRIAN GENERATOR FOR DEEP LEARNING PROBLEMS. Presented at 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING Inst Elect \& Elect Engineers; Inst Elect \& Elect Engineers, Signal Proc Soc

Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars (2020)
Journal Article
Hu, Y., Shum, H. P., & Ho, E. S. (2020). Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars. IET Intelligent Transport Systems, 14(13), 1845-1854. https://doi.org/10.1049/iet-its.2020.0439

The control of self-driving cars has received growing attention recently. Although existing research shows promising results in the vehicle control using video from a monocular dash camera, there has been very limited work on directly learning vehicl... Read More about Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars.

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., Zain, R. B., Jayasinghe, R. D., Rimal, J., Kerr, A. R., Amtha, R., Patil, K., Tilakaratne, W. M., Gibson, J., Cheong, S. C., & 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

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications (2020)
Journal Article
Bhattacharya, M., Roy, S., Mistry, K., Shum, H. P., & Chattopadhyay, S. (2020). A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications. IEEE Access, 8, 221330 - 221351. https://doi.org/10.1109/ACCESS.2020.3043621

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years. They necessitate fundamental s... Read More about A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications.

A plug-in attribute correction module for generalized zero-shot learning (2020)
Journal Article
Zhang, H., Bai, H., Long, Y., Liu, L., & Shao, L. (2021). A plug-in attribute correction module for generalized zero-shot learning. Pattern Recognition, 112, Article 107767. https://doi.org/10.1016/j.patcog.2020.107767

While Zero Shot Learning models can recognize new classes without training examples, they often fails to incorporate both seen and unseen classes together at the test time, which is known as the Generalized Zero-shot Learning (GZSL) problem. This pap... Read More about A plug-in attribute correction module for generalized zero-shot learning.

Modality independent adversarial network for generalized zero shot image classification (2020)
Journal Article
Zhang, H., Wang, Y., Long, Y., Yang, L., & Shao, L. (2021). Modality independent adversarial network for generalized zero shot image classification. Neural Networks, 134, 11-22. https://doi.org/10.1016/j.neunet.2020.11.007

Zero Shot Learning (ZSL) aims to classify images of unseen target classes by transferring knowledge from source classes through semantic embeddings. The core of ZSL research is to embed both visual representation of object instance and semantic descr... Read More about Modality independent adversarial network for generalized zero shot image classification.

A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction (2020)
Journal Article
Men, Q., Ho, E. S., Shum, H. P., & Leung, H. (2021). A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3417-3432. https://doi.org/10.1109/tcsvt.2020.3038145

Recurrent neural network (RNN) has become popular for human motion prediction thanks to its ability to capture temporal dependencies. However, it has limited capacity in modeling the complex spatial relationship in the human skeletal structure. In th... Read More about A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction.

Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning (2020)
Journal Article
Hu, J., Niu, H., Carrasco, J., Lennox, B., & Arvin, F. (2020). Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 69(12), https://doi.org/10.1109/tvt.2020.3034800

Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its useful... Read More about Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning.

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models (2020)
Book Chapter
Gajbhiye, A., Winterbottom, T., Al Moubayed, N., & Bradley, S. (2020). Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2020 (633-646). Springer Verlag. https://doi.org/10.1007/978-3-030-61609-0_50

We consider the task of incorporating real-world commonsense knowledge into deep Natural Language Inference (NLI) models. Existing external knowledge incorporation methods are limited to lexical-level knowledge and lack generalization across NLI mode... Read More about Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.

Facial reshaping operator for controllable face beautification (2020)
Journal Article
Hu, S., Shum, H. P., Liang, X., Li, F. W., & Aslam, N. (2021). Facial reshaping operator for controllable face beautification. Expert Systems with Applications, 167, Article 114067. https://doi.org/10.1016/j.eswa.2020.114067

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while... Read More about Facial reshaping operator for controllable face beautification.

On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery (2020)
Presentation / Conference Contribution
Wang, Q., Bhowmik, N., & Breckon, T. (2020, July). On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at International Joint Conference on Neural Networks, Glasgow, Scotland

X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of exte... Read More about On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery.

Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery (2020)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Isaac-Medina, B., & Breckon, T. (2020, September). Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery. Presented at Spie Security + Defence

Object detection from infrared-band (thermal) imagery has been a challenging problem for many years. With the advent of deep Convolutional Neural Networks (CNN), the automated detection and classification of objects of interest within the scene has b... Read More about Visible to Infrared Transfer Learning as a Paradigm for Accessible Real-time Object Detection and Classification in Infrared Imagery.

Target‐driven cloud evolution using position‐based fluids (2020)
Journal Article
Zhang, Z., Li, Y., Yang, B., Li, F. W., & Liang, X. (2020). Target‐driven cloud evolution using position‐based fluids. Computer Animation and Virtual Worlds, 31(6), https://doi.org/10.1002/cav.1937

To effectively control particle‐based cloud evolution without imposing strict position constraints, we propose a novel method integrating a control force field and a phase transition control into the position‐based fluids (PBF) framework. To produce... Read More about Target‐driven cloud evolution using position‐based fluids.

Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends (2020)
Journal Article
Schranz, M., Di Caro, G. A., Schmickl, T., Elmenreich, W., Arvin, F., Şekercioğlu, A., & Sende, M. (2021). Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends. Swarm and Evolutionary Computation, 60, Article 100762. https://doi.org/10.1016/j.swevo.2020.100762

Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, re... Read More about Swarm Intelligence and cyber-physical systems: Concepts, challenges and future trends.

On Modality Bias in the TVQA Dataset (2020)
Presentation / Conference Contribution
Winterbottom, T., Xiao, S., McLean, A., & Al Moubayed, N. (2020, September). On Modality Bias in the TVQA Dataset. Presented at The British Machine Vision Conference (BMVC), Manchester, England

TVQA is a large scale video question answering (video-QA) dataset based on popular TV shows. The questions were specifically designed to require “both vision and language understanding to answer”. In this work, we demonstrate an inherent bias in the... Read More about On Modality Bias in the TVQA Dataset.