Computer Vision, Perception and Cognition Group
“AI is THE key technology of the digital transformation, across sectors and industries, with major effects on our societies. Our research thus makes major contributions to the development of robust and trustworthy AI methods, and we enthusiastically teach their safe implementation and application.”
- Pattern recognition with deep learning
- Machine perception, computer vision and speaker recognition
- Neural system development
The CVPC group conducts pattern recognition research, working on a wide variety of tasks relating to image, audio, and signal data per se. We focus on deep neural network and reinforcement learning methodology, inspired by biological learning. Each task we study has its own learning target (e.g., detection, classification, clustering, segmentation, novelty detection, control) and corresponding use case (e.g., predictive maintenance, speaker recognition for multimedia indexing, document analysis, optical music recognition, computer vision for industrial quality control, automated machine learning, deep reinforcement learning for automated game play or building control), which in turn sheds light on different aspects of the learning process. We use this experience to create increasingly general AI systems built on neural architectures.
- Insight: keynotes, trainings
- AI consultancy: workshops, expert support, advise, technology assessment
- Research and development: small to large-scale collaborative projects, third party-funded research, student projects, commercially applicable prototypes
Feasibility Study Reinforcement Learning for Heating Systems
FarmAI – Artificial intelligence for Farming Simulator
Libra: A One-Tool Solution for MLD4 Compliance
Compared with earlier regulations, the 4th European Money Laundering Directive (MLD4) imposes rigorously increased requirements. It compels obliged entities to conduct in depth screenings of customers and their associations. The Libra Project aims at providing a one tool solution for meeting MLD4 compliance. The ...
DeepText: Intelligent Text Analysis with Deep Learning
DeepText develops a software framework to automatically analyse texts in order to extract important information. The framework comprises modern algorithms from the field of machine learning (deep learning) that are better at analyzing texts than traditional approaches. They can for example be used to extract ...
DeLLA: Deep-Learning-based speech recognition with limited training material
Speech recognition systems baed on Deep Neural Networks (DNN) currently brake all records and is being applied already in different products. These systems normally are trained with thousands of hours of training material for applications and languages where these amounts of data are available. In this feasibility ...
Delaux, Alexandre; Saint Aubert, Jean‐Baptiste; Ramanoël, Stephen; Bécu, Marcia; Gehrke, Lukas; Klug, Marius; Chavarriaga, Ricardo; Sahel, José‐Alain; Gramann, Klaus; Arleo, Angelo,
European Journal of Neuroscience.
54(12), pp. 8256-8282.
Available from: https://doi.org/10.1111/ejn.15190
Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.
1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.
Available from: https://doi.org/10.21256/zhaw-22061
Bontempi, Gianluca; Chavarriaga, Ricardo; De Canck, Hans; Girardi, Emanuela; Hoos, Holger; Kilbane-Dawe, Iarla; Ball, Tonio; Nowé, Ann; Sousa, Jose; Bacciu, Davide; Aldinucci, Marco; De Domenico, Manlio; Saffiotti, Alessandro; Maratea, Marco,
Ethics and Information Technology.
23(Suppl 1), pp. S127-S133.
Available from: https://doi.org/10.1007/s10676-020-09567-7
Aydarkhanov, Ruslan; Ušćumlić, Marija; Chavarriaga, Ricardo; Gheorghe, Lucian; Millán, José del R,
Journal of Neural Engineering.
18(2), pp. 026010.
Available from: https://doi.org/10.1088/1741-2552/abdfb2
Jao, Ping-Keng; Chavarriaga, Ricardo; Dell'Agnola, Fabio; Arza, Adriana; Atienza, David; Millan, Jose del R.,
IEEE Transactions on Human-Machine Systems.
51(2), pp. 99-108.
Available from: https://doi.org/10.1109/THMS.2020.3038339