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Outputs (200)

DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume (2023)
Journal Article
Miao, X., Bai, Y., Duan, H., Huang, Y., Wan, F., Xu, X., Long, Y., & Zheng, Y. (2024). DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume. IEEE Transactions on Circuits and Systems for Video Technology, 34(4), 2564-2576. https://doi.org/10.1109/tcsvt.2023.3305776

Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in dynamic objects in scenarios, l... Read More about DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume.

Using deep learning to analyze the psychological effects of COVID-19 (2023)
Journal Article
Almeqren, M. A., Almegren, M., Alhayan, F., Cristea, A. I., & Pennington, D. R. (2023). Using deep learning to analyze the psychological effects of COVID-19. Frontiers in Psychology, 14, Article 962854. https://doi.org/10.3389/fpsyg.2023.962854

Problem: Sentiment Analysis (SA) automates the classification of the sentiment of people’s attitudes, feelings or reviews employing natural language processing (NLP) and computational approaches. Deep learning has recently demonstrated remarkable suc... Read More about Using deep learning to analyze the psychological effects of COVID-19.

Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening (2023)
Presentation / Conference Contribution
Issac-Medina, B., Yucer, S., Bhowmik, N., & Breckon, T. (2023, June). Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection perfor... Read More about Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening.

Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery (2023)
Presentation / Conference Contribution
Gaus, Y., Bhowmik, N., Issac-Medina, B., Atapour-Abarghouei, A., Shum, H., & Breckon, T. (2023, June). Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC

Anomaly detection is a classical problem within automated visual surveillance, namely the determination of the normal from the abnormal when operational data availability is highly biased towards one class (normal) due to both insufficient sample siz... Read More about Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery.

The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation (2023)
Journal Article
Cristea, A. I., Alamri, A., Alshehri, M., Dwan Pereira, F., Toda, A. M., Harada T. de Oliveira, E., & Stewart, C. (2024). The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation. User Modeling and User-Adapted Interaction, 34(2), 323-374. https://doi.org/10.1007/s11257-023-09374-x

Massive Online Open Course (MOOC) platforms are considered a distinctive way to deliver a modern educational experience, open to a worldwide public. However, student engagement in MOOCs is a less explored area, although it is known that MOOCs suffer... Read More about The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation.

Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding (2023)
Presentation / Conference Contribution
Mohamed, H. A., Vascon, S., Hibraj, F., James, S., Pilutti, D., Del Bue, A., & Pelillo, M. (2022, August). Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding. Presented at International Conference on Pattern Recognition (ICPR) 2022 International Workshops and Challenge, Montreal, Canada

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition (2023)
Journal Article
Li, T., Wen, Z., Long, Y., Hong, Z., Zheng, S., Yu, L., …Shao, L. (2023). The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 13730-13748. https://doi.org/10.1109/tpami.2023.3294505

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio... Read More about The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

On Fine-tuned Deep Features for Unsupervised Domain Adaptation (2023)
Presentation / Conference Contribution
Wang, Q., Meng, F., & Breckon, T. (2023, June). On Fine-tuned Deep Features for Unsupervised Domain Adaptation. Presented at IJCNN 2023: International Joint Conference on Neural Networks, Queensland, Australia

Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the specific source or target domain data for a particular domain adaptati... Read More about On Fine-tuned Deep Features for Unsupervised Domain Adaptation.

Addressing Performance Inconsistency in Domain Generalization for Image Classification (2023)
Presentation / Conference Contribution
Stirling, J., & Moubayed, N. A. (2023, June). Addressing Performance Inconsistency in Domain Generalization for Image Classification. Presented at 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia

Domain Generalization (DG) in computer vision aims to replicate the human ability to generalize well under a shift of data distribution, or domain. In recent years, the field of domain generalization has seen a steady increase in average left-out tes... Read More about Addressing Performance Inconsistency in Domain Generalization for Image Classification.