• Role: PI
• Funding Source: NVIDIA Corporation
• Duration: 2015 – 2015

Overview

Fast Graphical Processing Units (GPUs) are key enablers for current deep learning methods in computer vision. We are grateful to the NVIDIA corporation for supporting our research in this area. Currently we are using a donated Tesla K40c GPU to train models for several different tasks.

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.
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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. 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.
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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}
   }
   

4. Bessinger Z, Jacobs N. 2016. Quantifying Curb Appeal. In: IEEE International Conference on Image Processing (ICIP). DOI: 10.1109/ICIP.2016.7533189.
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   @inproceedings{bessinger2016curbappeal,
     annotation = {nvidia},
     author = {Bessinger, Zachary and Jacobs, Nathan},
     booktitle = {IEEE International Conference on Image Processing (ICIP)},
     pdf = {https://zachbessinger.com/assets/papers/bessinger2016quantifying.pdf},
     note = {Acceptance rate: 45\%},
     project = {https://zachbessinger.com/publications/bessinger2016quantifying/},
     title = {Quantifying Curb Appeal},
     doi = {10.1109/ICIP.2016.7533189},
     year = {2016}
   }
   

5. Zhai M, Workman S, Jacobs N. 2016. Detecting Vanishing Points using Global Image Context in a Non-Manhattan World. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR.2016.610.
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   @inproceedings{zhai2016horizon,
     annotation = {spotlight,hlw,calibration,deeplearning,nvidia,contextualeyes},
     author = {Zhai, Menghua and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
     code = {https://github.com/viibridges/gc-horizon-detector},
     pdf = {http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhai_Detecting_Vanishing_Points_CVPR_2016_paper.pdf},
     thumbnail = {/thumbnails/horizon_sampling.jpg},
     title = {Detecting Vanishing Points using Global Image Context in a Non-{Manhattan} World},
     note = {Acceptance rate: 29.9\%},
     doi = {10.1109/CVPR.2016.610},
     year = {2016}
   }
   

6. Salem T, Workman S, Zhai M, Jacobs N. 2016. Analyzing Human Appearance as a Cue for Dating Images. In: IEEE Winter Conference on Applications of Computer Vision (WACV). 1–8. DOI: 10.1109/WACV.2016.7477678.
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   @inproceedings{salem2016face2year,
     annotation = {dataset_face2year,geofaces,contextualeyes,nvidia,social_media},
     author = {Salem, Tawfiq and Workman, Scott and Zhai, Menghua and Jacobs, Nathan},
     booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
     pages = {1--8},
     pdf = {https://drive.google.com/file/d/1CC61uyv6tWhUUex7ISAFHdPzdJC7XKSP/view?usp=sharing},
     doi = {10.1109/WACV.2016.7477678},
     project = {https://tsalem.github.io/audio-mapping/face2year/},
     title = {Analyzing Human Appearance as a Cue for Dating Images},
     note = {Acceptance rate: 42.3\%},
     year = {2016}
   }
   

7. Mihail RP, Workman S, Bessinger Z, Jacobs N. 2016. Sky Segmentation in the Wild: An Empirical Study. In: IEEE Winter Conference on Applications of Computer Vision (WACV). 1–6. DOI: 10.1109/WACV.2016.7477637.
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   @inproceedings{mihail2016sky,
     annotation = {contextualeyes,dataset_skyfinder,skyfinder,nvidia,timelapse},
     author = {Mihail, Radu Paul and Workman, Scott and Bessinger, Zach and Jacobs, Nathan},
     booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
     pages = {1--6},
     doi = {10.1109/WACV.2016.7477637},
     pdf = {https://drive.google.com/file/d/1-76FxbBQW8oD2m0JnCkZ7RTz-t541TiD/view?usp=sharing},
     project = {https://mvrl.github.io/SkyFinder/},
     title = {Sky Segmentation in the Wild: An Empirical Study},
     note = {Acceptance rate: 42.3\%},
     year = {2016}
   }
   

8. Baltenberger R, Zhai M, Greenwell C, Workman S, Jacobs N. 2016. A Fast Method for Estimating Transient Scene Properties. In: IEEE Winter Conference on Applications of Computer Vision (WACV). 1–8. DOI: 10.1109/WACV.2016.7477713.
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   @inproceedings{baltenberger2016transient,
     annotation = {contextualeyes,nvidia,timelapse,localization},
     author = {Baltenberger, Ryan and Zhai, Menghua and Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
     booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
     code = {https://github.com/mvrl/deeptransient/},
     pages = {1--8},
     pdf = {https://drive.google.com/file/d/1nIhSNqwduodSQOWh7qUNRKHEK_rWx2ZC/view?usp=sharing},
     project = {https://rbalten.github.io/transient/},
     doi = {10.1109/WACV.2016.7477713},
     title = {A Fast Method for Estimating Transient Scene Properties},
     note = {Acceptance rate: 42.3\%},
     year = {2016}
   }
   

9. Workman S, Zhai M, Jacobs N. 2016. Horizon Lines in the Wild. In: British Machine Vision Conference (BMVC).
   bibtex | paper | website | code

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   @inproceedings{workman2016hlw,
     annotation = {hlw,dataset_hlw,spotlight,waldo,nvidia,localization,calibration},
     author = {Workman, Scott and Zhai, Menghua and Jacobs, Nathan},
     booktitle = {British Machine Vision Conference (BMVC)},
     note = {Acceptance rate: 39.4\%},
     code = {https://github.com/scottworkman/deephorizon},
     pdf = {https://arxiv.org/pdf/1604.02129},
     project = {/datasets/hlw/},
     thumbnail = {/thumbnails/hlw.jpg},
     title = {Horizon Lines in the Wild},
     year = {2016}
   }
   

10. Jacobs N, Workman S, Zhai M. 2016. Crossview Convolutional Networks. In: IEEE Applied Imagery and Pattern Recognition (AIPR). DOI: 10.1109/AIPR.2016.8010593.
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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}
    }
    

11. Workman S, Souvenir R, Jacobs N. 2015. Wide-Area Image Geolocalization with Aerial Reference Imagery. In: IEEE International Conference on Computer Vision (ICCV). 1–9. DOI: 10.1109/ICCV.2015.451.
    bibtex | paper | website | doi | code

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    @inproceedings{workman2015localize,
      annotation = {spotlight,ugrad,dataset_cvusa,waldo,nvidia,remote_sensing,social_media,localization},
      author = {Workman, Scott and Souvenir, Richard and Jacobs, Nathan},
      booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      code = {https://github.com/scottworkman/deeplyfound},
      pages = {1--9},
      pdf = {http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Workman_Wide-Area_Image_Geolocalization_ICCV_2015_paper.pdf},
      project = {http://cs.uky.edu/%7escott/research/deeplyfound/},
      thumbnail = {/thumbnails/crossview.jpg},
      title = {Wide-Area Image Geolocalization with Aerial Reference Imagery},
      doi = {10.1109/ICCV.2015.451},
      note = {Acceptance rate: 30.3\%},
      year = {2015}
    }
    

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.