CAPE: Context-Aware Private Embeddings for Private Language Learning
Conference Proceeding
Plant, R., Gkatzia, D., & Giuffrida, V. (2021)
CAPE: Context-Aware Private Embeddings for Private Language Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (7970-7978
Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. Howe...
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour
Conference Proceeding
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2020)
Towards Continuous User Authentication Using Personalised Touch-Based Behaviour. In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00023
In this paper, we present an empirical evaluation of 30 features used in touch-based continuous authentication. It is essential to identify the most significant features for e...
Understanding Deep Neural Networks For Regression In Leaf Counting
Conference Proceeding
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2020)
Understanding Deep Neural Networks For Regression In Leaf Counting. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). , (4321-4329). https://doi.org/10.1109/CVPRW.2019.00316
Deep learning methods are constantly increasing in popularity and success across a wide range of computer vision applications. However, they are perceived as 'black boxes', du...
Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation
Conference Proceeding
Giuffrida, M. V., Dobrescu, A., Doerner, P., & Tsaftaris, S. A. (2019)
Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation
Deep learning is making strides in plant phenotyping and agriculture. But pretrained models require significant adaptation to work on new target datasets originating from a di...
Adversarial Large-scale Root Gap Inpainting
Conference Proceeding
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2019)
Adversarial Large-scale Root Gap Inpainting
Root imaging of a growing plant in a non-invasive, affordable , and effective way remains challenging. One approach is to image roots by growing them in a rhizobox, a soil-fil...
Root Gap Correction with a Deep Inpainting Model
Conference Proceeding
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018)
Root Gap Correction with a Deep Inpainting Model
Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most aff...
Leveraging multiple datasets for deep leaf counting
Conference Proceeding
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2018)
Leveraging multiple datasets for deep leaf counting. https://doi.org/10.1109/ICCVW.2017.243
The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for c...
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
Conference Proceeding
Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A. (2017)
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network. https://doi.org/10.1109/ICCVW.2017.242
In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems ,...
Whole Image Synthesis Using a Deep Encoder-Decoder Network
Conference Proceeding
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016)
Whole Image Synthesis Using a Deep Encoder-Decoder Network. In Simulation and Synthesis in Medical Imaging. , (127-137). https://doi.org/10.1007/978-3-319-46630-9_13
The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI thi...
On Blind Source Camera Identification
Conference Proceeding
Farinella, G. M., Giuffrida, M. V., Digiacomo, V., & Battiato, S. (2015)
On Blind Source Camera Identification. In Advanced Concepts for Intelligent Vision Systems. , (464-473). https://doi.org/10.1007/978-3-319-25903-1_40
An interesting and challenging problem in digital image forensics is the identification of the device used to acquire an image. Although the source imaging device can be retri...