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Outputs (224)

Modeling Detailed Cloud Scene from Multi-source Images (2018)
Presentation / Conference Contribution
Cen, Y., Liang, X., Chen, J., Yang, B., & Li, F. W. (2018, December). Modeling Detailed Cloud Scene from Multi-source Images. Presented at Pacific Graphics, Hong Kong

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For e... Read More about Modeling Detailed Cloud Scene from Multi-source Images.

Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery (2018)
Book Chapter
Payen de La Garanderie, G., Atapour Abarghouei, A., & Breckon, T. P. (2018). Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 : 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XII (812-830). Springer Verlag. https://doi.org/10.1007/978-3-030-01261-8_48

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360... Read More about Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018, July). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. Presented at 2018 IEEE World Congress on Computational Intelligence (WCCI 2018)., Rio de Janeiro, Brazil

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

Attribute relaxation from class level to instance level for zero-shot learning (2018)
Journal Article
Zhang, H., Long, Y., & Zhao, C. (2018). Attribute relaxation from class level to instance level for zero-shot learning. Electronics Letters, 54(20), 1170-1172. https://doi.org/10.1049/el.2018.5027

Conventional zero-shot learning (ZSL) methods usually use class-level attribute, which corresponds to a batch of images of same category. This setting is not reasonable since the images even though belong to same category still have variances in thei... Read More about Attribute relaxation from class level to instance level for zero-shot learning.

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018, December). An Exploration of Dropout with RNNs for Natural Language Inference. Presented at ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.

Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum (2018)
Presentation / Conference Contribution
Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018, December). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. Presented at 2018 ACM Conference on Information and Knowledge Management (CIKM’18), Torino

Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general election... Read More about Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum.