Outputs (13)

CAPE: Context-Aware Private Embeddings for Private Language Learning (2021)
Presentation / Conference Contribution
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. However, obtaining text representations or embeddings using these models risks... Read More about CAPE: Context-Aware Private Embeddings for Private Language Learning.

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses (2021)
Presentation / Conference Contribution
Frangulea, M., Pantos, C., Giuffrida, V., & Valente, J. (2021). Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses. In Precision agriculture ’21 (493-500). https://doi.org/10.3920/978-9

A tool for plant phenotyping is proposed to aid users in analyzing data on-demand. This tool is web-based and runs deep learning models. The current study focuses on the development of this tool, as well as obtaining a plant dataset to train a neural... Read More about Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses.

Towards Continuous User Authentication Using Personalised Touch-Based Behaviour (2020)
Presentation / Conference Contribution
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

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 each user, as behaviour is different amongst humans. Thus, a fixed feature s... Read More about Towards Continuous User Authentication Using Personalised Touch-Based Behaviour.

Understanding Deep Neural Networks For Regression In Leaf Counting (2020)
Presentation / Conference Contribution
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/C

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', due to the lack of an intuitive interpretation of their decision processes. W... Read More about Understanding Deep Neural Networks For Regression In Leaf Counting.

Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation (2019)
Presentation / Conference Contribution
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 different experiment even on the same species. The current solution is to ret... Read More about Leaf Counting Without Annotations Using Adversarial Unsupervised Domain Adaptation.

Adversarial Large-scale Root Gap Inpainting (2019)
Presentation / Conference Contribution
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-filled transparent container, imaging them with digital cameras, and segmentin... Read More about Adversarial Large-scale Root Gap Inpainting.

Root Gap Correction with a Deep Inpainting Model (2018)
Presentation / Conference Contribution
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 affordable and diffuse approaches is the use of mesocosms, where plants are gr... Read More about Root Gap Correction with a Deep Inpainting Model.

Leveraging multiple datasets for deep leaf counting (2018)
Presentation / Conference Contribution
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2017, October). Leveraging multiple datasets for deep leaf counting. Presented at 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy

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 counting leaves in model rosette plants. While state-of-the-art results on l... Read More about Leveraging multiple datasets for deep leaf counting.

ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network (2017)
Presentation / Conference Contribution
Giuffrida, M. V., Scharr, H., & Tsaftaris, S. A. (2017, October). ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network. Presented at 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy

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 , such as leaf segmentation (a multi-instance problem) and counting. Most of... Read More about ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network.

Whole Image Synthesis Using a Deep Encoder-Decoder Network (2016)
Presentation / Conference Contribution
Sevetlidis, V., Giuffrida, M. V., & Tsaftaris, S. A. (2016, October). Whole Image Synthesis Using a Deep Encoder-Decoder Network. Presented at SASHIMI Workshop - MICCAI, Athens, Greece

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 this represents the synthesis of images originating from different MR sequence... Read More about Whole Image Synthesis Using a Deep Encoder-Decoder Network.