Multimodal Vision Research Laboratory MVRL
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Amazon Web Services Grant

The Amazon Corporation has long been a supporter of academic research in computer vision, from their fantastic Mechanical Turk site to the ability to quickly create large-scale clusters for image processing. We are grateful for their support of our research in processing social network imagery.

Related Publication(s)

  1. Thumbnail for Implicit Land Use Mapping Using Social Media Imagery
    Greenwell C, Workman S, Jacobs N. 2019. Implicit Land Use Mapping Using Social Media Imagery. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR47015.2019.9174570.
    paper doi bibtex GeoAI Urban
  2. Thumbnail for Predicting Ground-Level Scene Layout from Aerial Imagery
    Zhai M, Bessinger Z, Workman S, Jacobs N. 2017. Predicting Ground-Level Scene Layout from Aerial Imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2017.440.
    code paper doi bibtex GenAI Localization GeoAI Urban
  3. Thumbnail for Who Goes There? Approaches to Mapping Facial Appearance Diversity
    Bessinger Z, Stauffer C, Jacobs N. 2016. Who Goes There? Approaches to Mapping Facial Appearance Diversity. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). DOI: 10.1145/2996913.2996997.
    paper doi bibtex Urban
  4. Thumbnail for Crossview Convolutional Networks
    Jacobs N, Workman S, Zhai M. 2016. Crossview Convolutional Networks. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR.2016.8010593.
    paper doi bibtex GeoAI Urban

Acknowledgements

This work was supported by AWS in Education Grant award.

Grant Details

  • Role: PI
  • Funding Source: Amazon
  • Budget: $5000
  • Duration: 2015 – 2016
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