Context-adaptive Fusion

Context-Adaptive Fusion is a context-aware fusion strategy that dynamically adjusts the integration process of multimodal or heterogeneous data based on the prevailing contextual conditions, distinguishing itself by its ability to modify fusion mechanisms in real time according to environmental, situational, or operational context.

32

Publications

1.3K

Citations

144

Authors

63

Institutions

Publications per year

2017–2026

21

Authors

144

  1. JL
    James Llinas

    University at Buffalo, State University of New York, United States

    2 publications · 96 citations

    2

    h-index

  2. SA
    Saeed Anwar

    Australian National University, Australia

    2 publications · 283 citations

    2

    h-index

  3. NB
    Nick Barnes

    Australian National University, Australia

    2 publications · 283 citations

    2

    h-index

  4. SQ
    Shi Qiu

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 283 citations

    2

    h-index

  5. MF
    Mihai Florea

    Thales (Canada), Canada

    1 publications · 17 citations

    1

    h-index

1–5 of 144

Venues

  1. IEEE Transactions on Multimedia

    2 publications · 75 citations

    2

    h-index

  2. Information Fusion

    2 publications · 58 citations

    2

    h-index

  3. Signal Processing Image Communication

    1 publications · 22 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 100 citations

    1

    h-index

  5. 1

    h-index

1–5