• Role: PI
• Funding Source: Amazon
• Duration: 2015 – 2016

Overview

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. 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.
   bibtex | paper | doi

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   @inproceedings{greenwell2019implicit,
     annotation = {abstract,google_crossview,amazon,nvidia,remote_sensing,social_media,cvusa},
     author = {Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Applied Imagery and Pattern Recognition (AIPR)},
     title = {Implicit Land Use Mapping Using Social Media Imagery},
     note = {(oral)},
     doi = {10.1109/AIPR47015.2019.9174570},
     pdf = {https://drive.google.com/open?id=1XAmhngKoXOKfAqUwRBCOhCWSBLQ84mqR},
     year = {2019}
   }
   

2. 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.
   bibtex | paper | doi | tweet | code

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   @inproceedings{zhai2017layout,
     annotation = {google_crossview,synthesis,localization,spotlight,nvidia,amazon,career,remote_sensing,cvusa},
     author = {Zhai, Menghua and Bessinger, Zach and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     code = {https://github.com/viibridges/crossnet},
     doi = {10.1109/CVPR.2017.440},
     note = {Acceptance rate: 29.2\%},
     pdf = {https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.pdf},
     tweet = {https://twitter.com/jacobsn/status/837012306814980097},
     thumbnail = {/thumbnails/predict_layout.jpg},
     title = {Predicting Ground-Level Scene Layout from Aerial Imagery},
     year = {2017}
   }
   

3. Jacobs N, Workman S, Zhai M. 2016. Crossview Convolutional Networks. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR.2016.8010593.
   bibtex | paper | doi

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   @inproceedings{jacobs2016crossview,
     annotation = {google_crossview,amazon,nvidia,remote_sensing,abstract,social_media},
     author = {Jacobs, Nathan and Workman, Scott and Zhai, Menghua},
     booktitle = {IEEE Applied Imagery and Pattern Recognition (AIPR)},
     title = {Crossview Convolutional Networks},
     note = {(oral)},
     pdf = {https://drive.google.com/open?id=11Nklh0ywRjSCsyYzAlQooTzMydRA8UPK},
     doi = {10.1109/AIPR.2016.8010593},
     year = {2016}
   }
   

4. 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.
   bibtex | paper | doi

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   @inproceedings{bessinger2016wgt,
     annotation = {dataset_wgt,geolookbook,geofaces,ugrad,amazon,nvidia,social_media},
     author = {Bessinger, Zachary and Stauffer, Chris and Jacobs, Nathan},
     booktitle = {ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL)},
     title = {Who Goes There? {Approaches} to Mapping Facial Appearance Diversity},
     pdf = {https://drive.google.com/open?id=1-5J0puHLPsM7cYDwWjf9fu8RP9qeaT9h},
     doi = {10.1145/2996913.2996997},
     year = {2016}
   }
   

Acknowledgements

This work was supported by AWS in Education Grant award.