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.”
Fields of expertise

- 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.
Services
- 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
Team
Head of Research Group
Projects
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DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology
_Management AbstractPlaying and enjoying music is amongst the most rewarding recreational activities of humankind for individuals as well as in group settings. Visiting concerts or sending one’s kids to music lessons - thus being enabled to discuss and co-shape the musical part of our culture - are hence important ...
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Complexity 4.0
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PANOPTES
The new product of ARGUS DATA INSIGHTS Schweiz AG "Real Time Print Media Monitoring" is an automated pipeline. It identifies relevant articles in print media, extracts them and sends them to the customers in real-time. Core of this project is the automated segmentation of full newspaper pages into articles, this is ...
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MobileMall
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SODES: Swiss Open Data Exploration System
In recent years, national and international institutions, governments and NGOs have made large amounts of data publicly available: there exist literally thousands of open data sources, with temperature measurements, stock market prices, population and income statistics etc. However, most open data sets are provided ...
Publications
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Perdikis, Serafeim; Leeb, Robert; Chavarriaga, Ricardo; Millán, José del R.,
2020.
Context-aware learning for generative models.
IEEE Transactions on Neural Networks and Learning Systems.
Available from: https://doi.org/10.1109/TNNLS.2020.3011671
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2020.
Standards for neurotechnologies and brain-machine interfacing.
IEEE Systems, Man, and Cybernetics Magazine.
6(3), pp. 50-51.
Available from: https://doi.org/10.1109/MSMC.2020.2995438
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Roost, Dano; Meier, Ralph; Huschauer, Stephan; Nygren, Erik; Egli, Adrian; Weiler, Andreas; Stadelmann, Thilo,
2020.
Improving sample efficiency and multi-agent communication in RL-based train rescheduling [paper].
In:
Proceedings of the 7th SDS.
7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020.
IEEE.
Available from: https://doi.org/10.21256/zhaw-19978
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Aydarkhanov, Ruslan; Ušćumlić, Marija; Chavarriaga, Ricardo; Gheorghe, Lucian; del R Millán, José,
2020.
Spatial covariance improves BCI performance for late ERPs components with high temporal variability.
Journal of Neural Engineering.
17(3), pp. 036030.
Available from: https://doi.org/10.1088/1741-2552/ab95eb
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Orset, Bastien; Lee, Kyuhwa; Chavarriaga, Ricardo; Millan, Jose del R.,
2020.
User adaptation to closed-loop decoding of motor imagery termination.
IEEE Transactions on Biomedical Engineering.
68(1), pp. 3-10.
Available from: https://doi.org/10.1109/TBME.2020.3001981