Spatial Attention Networks

Spatial Attention Networks is a computational framework that selectively enhances relevant regions within a visual scene by modulating neural activity based on spatial location, thereby distinguishing it from other attention mechanisms through its explicit modeling of spatial relationships in perceptual processing.

2K

Publications

162.7K

Citations

7.2K

Authors

1.6K

Institutions

Publications per year

2017–2026

1.3K

Authors

7.2K

  1. GR
    George R. Mangun

    University of California, Davis, United States

    16 publications · 3.1K citations

    16

    h-index

  2. LJ
    Licheng Jiao

    Xidian University, China

    15 publications · 1.5K citations

    15

    h-index

  3. MC
    Maurizio Corbetta

    Washington University in St. Louis, United States

    11 publications · 5.3K citations

    11

    h-index

  4. XW
    Xiaogang Wang

    European Organization for Nuclear Research, Switzerland

    11 publications · 2K citations

    11

    h-index

  5. EM
    Emiliano Macaluso

    Fondazione Santa Lucia, Italy

    11 publications · 891 citations

    11

    h-index

1–5 of 7.2K

Venues

  1. arXiv (Cornell University)

    Repository · 82 publications · 6K citations

    36

    h-index

  2. 36

    h-index

  3. Neuropsychologia

    50 publications · 4.3K citations

    36

    h-index

  4. NeuroImage

    50 publications · 6.5K citations

    32

    h-index

  5. Remote Sensing

    65 publications · 4.7K citations

    31

    h-index

1–5