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Outputs (2845)

Geometric Features Enhanced Human-Object Interaction Detection (2024)
Journal Article
Zhu, M., Ho, E. S. L., Chen, S., Yang, L., & Shum, H. P. H. (2024). Geometric Features Enhanced Human-Object Interaction Detection. IEEE Transactions on Instrumentation and Measurement, 73, Article 5026014. https://doi.org/10.1109/TIM.2024.3427800

Cameras are essential vision instruments to capture images for pattern detection and measurement. Human–object interaction (HOI) detection is one of the most popular pattern detection approaches for captured human-centric visual scenes. Recently, Tra... Read More about Geometric Features Enhanced Human-Object Interaction Detection.

The complexity of computing optimum labelings for temporal connectivity (2024)
Journal Article
Klobas, N., Mertzios, G., Molter, H., & Spirakis, P. (2024). The complexity of computing optimum labelings for temporal connectivity. Journal of Computer and System Sciences, 146, Article 103564. https://doi.org/10.1016/j.jcss.2024.103564

A graph is temporally connected if a strict temporal path exists from every vertex u to every other vertex v. This paper studies temporal design problems for undirected temporally connected graphs. Given a connected undirected graph G, the goal is to... Read More about The complexity of computing optimum labelings for temporal connectivity.

Designing a Pedagogical Framework for Developing Abstraction Skills (2024)
Presentation / Conference Contribution
Begum, M., Crossley, J., Strömbäck, F., Akrida, E., Alpizar-Chacon, I., Evans, A., Gross, J. B., Haglund, P., Lonati, V., Satyavolu, C., & Thorgeirsson, S. (2024, July). Designing a Pedagogical Framework for Developing Abstraction Skills. Presented at ITiCSE 2024: Innovation and Technology in Computer Science Education, Milan Italy

Uncovering hidden and complex relations of pandemic dynamics using an AI driven system (2024)
Journal Article
Demirbaga, U., Kaur, N., & Aujla, G. S. (2024). Uncovering hidden and complex relations of pandemic dynamics using an AI driven system. Scientific Reports, 14(1), Article 15433. https://doi.org/10.1038/s41598-024-65845-0

The COVID-19 pandemic continues to challenge healthcare systems globally, necessitating advanced tools for clinical decision support. Amidst the complexity of COVID-19 symptomatology and disease severity prediction, there is a critical need for robus... Read More about Uncovering hidden and complex relations of pandemic dynamics using an AI driven system.

CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video (2024)
Journal Article
Miao, X., Bai, Y., Duan, H., Wan, F., Huang, Y., Long, Y., & Zheng, Y. (2024). CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video. Pattern Recognition, 156, Article 110729. https://doi.org/10.1016/j.patcog.2024.110729

The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performance by leveraging time-varying dynamic radiation fields. However, thes... Read More about CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video.

Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness (2024)
Journal Article
Song, C., Chen, Q., Li, F. W. B., Jiang, Z., Zheng, D., Shen, Y., & Yang, B. (2024). Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness. Visual Computer, 40, 4955–4967. https://doi.org/10.1007/s00371-024-03498-w

Self-supervised monocular depth estimation has opened up exciting possibilities for practical applications, including scene understanding, object detection, and autonomous driving, without the need for expensive depth annotations. However, traditiona... Read More about Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness.

Explainable text-tabular models for predicting mortality risk in companion animals (2024)
Journal Article
Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets contai... Read More about Explainable text-tabular models for predicting mortality risk in companion animals.

Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis. Geoenergy Science and Engineering, 240, Article 213011. https://doi.org/10.1016/j.geoen.2024.213011


The critical role of lithology classification in reservoir exploration is increasingly germinating interest in intelligent rock image classification applications. Nonetheless, the efficacy of these classification methods predominan... Read More about Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis.