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.

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

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

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

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

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

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

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

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

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...