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Publications

  1. Bonanomi, Cristian; Balletti, Simone; Lecca, Michela; Anisetti, Marco; Rizzi, Alessandro; Damiani, Ernesto,
    I3D: a new dataset for testing denoising and demosaicing algorithms,
    in «MULTIMEDIA TOOLS AND APPLICATIONS»,
    to be published
  2. Gottardi, Massimo; Lecca, Michela,
    in «IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS. I, REGULAR PAPERS»,
    vol. 66,
    n. 5,
    2019
    , pp. 1831 -
    1839
  3. Qian, Xinyuan; Brutti, Alessio; Lanz, Oswald; Omologo, Maurizio; Cavallaro, Andrea,
    in «IEEE TRANSACTIONS ON MULTIMEDIA»,
    2019
  4. Lecca, Michela; Messelodi, Stefano,
    in «JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION»,
    vol. 36,
    n. 8,
    2019
    , pp. 1423 -
    1432
  5. Lanz, Oswald; Brutti, Alessio; Xompero, Alessio; Qian, Xinyuan; Omologo, Maurizio; Cavallaro, Andrea,
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),
    2019
    , (IEEE International Conference on Acoustics, Speech, and Signal Processing,
    Brighton, UK,
    12 - 17 May, 2019)
  6. Sudhakaran, Swathikiran; Escalera, Sergio; Lanz, Oswald,
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR),
    2019
    , pp. 9954-
    9963
    , (IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
    Long Beach, CA,
    16 - 20 June, 2019)
  7. Ilyes Lakhal, Mohamed; Lanz, Oswald; Cavallaro, Andrea,
    Learnable masks for pose-guided view synthesis,
    Proceedings of the IEEE International Conference on Image Processing (ICIP),
    2019
    , (IEEE International Conference on Image Processing,
    Taipei, Taiwan,
    22-25 September)
  8. Sudhakaran, Swathikiran; Lanz, Oswald,
    CVPR19 Workshop - Mutual benefits of cognitive and computer vision,
    2019
    , (CVPR19 Workshop - Mutual benefits of cognitive and computer vision,
    Long Beach, CA,
    16 June, 2019)
  9. Rizzi, Alessandro; Plutino, Alice; Lecca, Michela,
    2019
  10. Sudhakaran, Swathikiran; Escalera, Sergio; Lanz, Oswald,
    In this report we describe the technical details of our submission to the EPIC-Kitchens 2019 action recognition challenge. To participate in the challenge we have developed a number of CNN-LSTA [3] and HF-TSN [2] variants, and submitted predictions from an ensemble compiled out of these two model families. Our submission, visible on the public leaderboard with team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 35.54% on S1 setting, and 20.25% on S2 setting.,
    2019

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