Multimodal Data Sampling

Multimodal Data Sampling is a methodological framework that systematically selects and integrates instances from multiple sensory modalities—such as text, audio, video, and sensor data—into a coherent dataset, distinguished from unimodal sampling by its explicit coordination of temporally and semantically aligned cross-modal signals for joint analysis.

15

Publications

1.1K

Citations

97

Authors

40

Institutions

Publications per year

2017–2026

10

Authors

97

  1. WA
    William A. P. Smith

    University of York, United Kingdom

    1 publications · 16 citations

    1

    h-index

  2. BD
    Bertrand Douillard

    The University of Sydney, Australia

    1 publications · 11 citations

    1

    h-index

  3. JW
    Jun Wang

    Southwestern University of Finance and Economics, China

    1 publications · 204 citations

    1

    h-index

  4. HT
    Harry T. Mason

    Queen Mary University of London, United Kingdom

    1 publications · 16 citations

    1

    h-index

  5. BR
    Bruce R. Muller

    Newcastle University, United Kingdom

    1 publications · 16 citations

    1

    h-index

1–5 of 97

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 1 publications · 12 citations

    1

    h-index

  2. The Journal of Early Adolescence

    1 publications · 226 citations

    1

    h-index

  3. Proceedings of the 30th ACM International Conference on Multimedia

    Conference · 1 publications · 48 citations

    1

    h-index

  4. IEEE Access

    1 publications · 30 citations

    1

    h-index

  5. eLife

    1 publications · 152 citations

    1

    h-index

1–5