Multimodal Context Recognition

Multimodal Context Recognition is a computational framework that integrates and analyzes data from multiple heterogeneous sensory modalities to infer high-level contextual states, distinguishing itself from unimodal approaches by leveraging complementary and cross-validated evidence across diverse input sources.

27

Publications

1.4K

Citations

113

Authors

35

Institutions

Publications per year

2017–2026

11

Authors

113

  1. PL
    Paul Lukowicz

    German Research Centre for Artificial Intelligence, Germany

    5 publications · 236 citations

    5

    h-index

  2. DB
    David Bannach

    University of Passau, Germany

    4 publications · 190 citations

    4

    h-index

  3. GT
    Gerhard Tröster

    ETH Zurich, Switzerland

    3 publications · 87 citations

    3

    h-index

  4. JD
    J. Doppler

    Universität Innsbruck, Austria

    2 publications · 55 citations

    2

    h-index

  5. MT
    Masahiko Tsukamoto

    Kobe University, Japan

    2 publications · 40 citations

    2

    h-index

1–5 of 113

Venues

  1. 2

    h-index

  2. IEEE Pervasive Computing

    2 publications · 127 citations

    2

    h-index

  3. Pervasive and Mobile Computing

    2 publications · 98 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 1 publications · 48 citations

    1

    h-index

  5. ACM Transactions on Graphics

    TOG · 1 publications · 304 citations

    1

    h-index

1–5