Department of
Environmental Sciences, Informatics and Statistics

Computer Vision and Machine Learning

The laboratory has been conducting research in the field of Artificial Intelligence, particularly in the areas of machine learning, computer vision and pattern recognition, since 1995.

Over the years, the laboratory has contributed to advancing the state of the art in these areas from a theoretical, methodological and applicative perspective, making it a reference point in the field both nationally and internationally.

The group has an extensive network of scientific collaborations and regularly participates, often in the role of coordinator, in European and national research projects.

Research group

Collaborators

  • Luca Palmieri (Postdoc)
  • Hebatallah Mohamed (Postdoc)
  • Diego Pilutti (Postdoc)
  • Alessandro Torcinovich (Adjunct Professor)
  • Antonio Emanuele Cinà (PhD Student)
  • Marina Khoroshiltseva (PhD Student)
  • Sara Ferro (PhD Student)
  • Waqar Ali (PhD Student)
  • Guglielmo Beretta (PhD Student)

Collaborations

Publications

  • I. Elezi, J. Seidenscharz, L.Wagner, S. Vascon, A. Torcinovich, M. Pelillo, and L. Leal-Taixé. The Group Loss++: A deeper look into group loss for deep metric learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (in press)
  • A. Cinà, A. Torcinovich, and M. Pelillo. A black-box adversarial attack for poisoning clustering. Pattern Recognition 122:108306 (2022)
  • S. Aslan, S. Vascon, and M. Pelillo. Two sides of the same coin: Improved ancient coin classification using graph transduction games. Pattern Recognition Letters 131:158-165 (2020)
  • E. Zemene, L. Alemu, and M. Pelillo. Dominant sets for "constrained" image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(10):2438-2451 (2019)
  • E. Zemene, Y. Tariku, H. Idrees, A. Prati, M. Pelillo, and M. Shah. Large-scale image geolocalization using dominant sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(1):148-161 (2019)

Research projects

  • RePAIR - Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage, 2021-2025 (H2020 FET-Open) - Scientific Coordinator
  • ELISE - European Learning and Intelligent Systems Excellence, 2020-2023 (H2020) - Third-arty Principal Investigator
  • Artificial Intelligence Assisted Performance and Anomaly Detection and Diagnostic, 2020-2021 (European Space Agency) - Principal Investigator
  • MEMEX - Memories and Experiences for Inclusive Digital Storytelling, 2020-2022 (H2020) - Third-arty Principal Investigator
  • AI4EU - A European AI On Demand Platform and Ecosystem, 2019-2021 (H2020), Principal Investigator
  • REXlearn - Reliable and Explainable Adversarial Machine Learning, 2019-2021 (PRIN), Principal Investigator
  • Hume-Nash Machines: Context-Aware Models of Learning and Recognition, 2015-2017 (Samsung Global Research Outreach Grant) - Sole Principal Investigator
  • Security Issues in Pattern Recognition, 2010-2013 (Regione Autonoma della Sardegna) - Principal Investigator
  • SIMBAD - Beyond Features: Similarity-Based Pattern Analysis and Recognition, 2008-2011 (FP7 FET-Open), Scientific Coordinator
  • Similarity-Based Methods for Computer Vision and Pattern Recognition: Theory, Algorithms, Applications, 2007-2009 (PRIN), Scientific Coordinator
  • Machine Learning Methods for Structural Genomics, 2002-2004 (PRIN), Principal Investigator
  • Neural Networks for Learning in Structural Domains: Methods and Applications, 2000-2002 (PRIN) - Principal Investigator

Last update: 17/04/2024