Papers with Code
Click on the title of the paper and you will be redirected to its specific page, where you can find a link to the repository. If you use any of the published pieces of code available, please do not forget to cite me and my co-authors. You will find a easy-to-use BibTex reference to add into your LaTeX file.
TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI
Generating realistic images to accurately predict changes in the structure of brain MRI can be a crucial tool for clinicians. Such applications can help assess patients’ outcomes and analyze how diseases progress at the individual level. However, existing methods developed for this task present some l...
Synchronization is All You Need: Exocentric-to-Egocentric Transfer for Temporal Action Segmentation with Unlabeled Synchronized Video Pairs
We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the collection and labeling of a new set of egocentric videos...
Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography
A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple or pear samples without disorders and outperformed a tradi...
On the cloud detection from backscattered images generated from a lidar-based ceilometer: Current state and opportunities
Accurate weather monitoring depends significantly on cloud detection, a crucial process achievable through remote sensing tools such as satellite imagery and radar or through the analysis of data obtained from ceilometers. A ceilometer is a lidar-based device allowing to analyse the atmosphere and detect...
Federated Learning in a Semi-Supervised Environment for Earth Observation Data
We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor...
Adapting Vision Foundation Models for Plant Phenotyping
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 retrain the model on the new target data implying the...
An omnidirectional approach to touch-based continuous authentication
This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. I...
Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap
This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. I...
CAPE: Context-Aware Private Embeddings for Private Language Learning
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others. Obtaining text representations or embeddings using these models presents the possibility of encoding personally identifiable...
Unsupervised Rotation Factorization in Restricted Boltzmann Machines
Finding suitable image representations for the task at hand is critical in computer vision. Different approaches extending the original Restricted Boltzmann Machine (RBM) model have recently been proposed to offer rotation-invariant feature learning. In this paper, we present an extended novel RBM that...
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 retrain the model on the new target data implying the...
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting
Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, requiring software that can reliably extract plant traits, such as leaf count, preferably across a variety of species and growth conditions....
An interactive tool for semi-automated leaf annotation
High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental...
Papers with Software
The following papers come with downloadle software (this may link to external websites). If you use any of the published pieces of software available, please do not forget to cite me and my co-authors. You will find a easy-to-use BibTex reference to add into your LaTeX file.
Phenotiki: An Open Software And Hardware Platform For Affordable And Easy Image-based Phenotyping Of Rosette-shaped Plants
Phenotyping is important to understand plant biology but current solutions are either costly, not versatile or difficult to deploy. To solve this problem, we present Phenotiki, an affordable system for plant phenotyping which, relying on off-the-shelf parts, provides an easy to install and maintain platform,...
Papers with Data
The following papers contain data (or datasets) (this may link to external websites). If you use any of the published datasets, please do not forget to cite me and my co-authors. You will find a easy-to-use BibTex reference to add into your LaTeX file.
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
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 these algorithms need labelled data to learn a model...