Multimodal Vision Research Laboratory

MVRL

about us

The Multimodal Vision Research Laboratory (MVRL) develops novel algorithms for image understanding and works to solve challenging problems in areas including remote sensing, image localization, and medical imaging. This summer we will be moving from the University of Kentucky to the Computer Science and Engineering department of Washington University in St. Louis.

If you are interested in joining us, please check out our openings page for more information and a description of current open positions.

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recent news

Check the archives for old news.

selected publications

See our publications page for a complete listing.
  1. PDF Workman S, Rafique MU, Blanton H, Jacobs N. 2022. Revisiting Near/Remote Sensing with Geospatial Attention. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
    bibtex
  2. PDF Padilha R, Salem T, Workman S, Andaló FA, Rocha A, Jacobs N. 2022. Content-Based Detection of Temporal Metadata Manipulation. IEEE Transactions on Information Forensics and Security:1316–1327. DOI: 10.1109/TIFS.2022.3159154.
    bibtex | website
  3. PDF Blanton H, Workman S, Jacobs N. 2022. A Structure-Aware Method for Direct Pose Estimation. In: IEEE Winter Conference on Applications of Computer Vision (WACV).
    bibtex
  4. PDF Zhang Y, Liang G, Jacobs N. 2021. Dynamic Feature Alignment for Semi-supervised Domain Adaptation. In: British Machine Vision Conference (BMVC).
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  5. PDF Liang G, Greenwell C, Zhang Y, Xing X, Wang X, Kavuluru R, Jacobs N. 2022. Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging. IEEE Journal of Biomedical and Health Informatics 26. DOI: 10.1109/JBHI.2021.3110805.
    bibtex
  6. PDF Rafique MU, Zhang Y, Brodie B, Jacobs N. 2021. Unifying Guided and Unguided Outdoor Image Synthesis. In: New Trends in Image Restoration and Enhancement (IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops). 776–785. DOI: 10.1109/CVPRW53098.2021.00087.
    bibtex | website
  7. PDF Liang G, Zhang Y, Wang X, Jacobs N. 2020. Improved Trainable Calibration Method for Neural Networks. In: British Machine Vision Conference (BMVC).
    bibtex | website
  8. PDF Rafique MU, Blanton H, Snavely N, Jacobs N. 2020. Generative Appearance Flow: A Hybrid Approach for Outdoor View Synthesis. In: British Machine Vision Conference (BMVC).
    bibtex | website | code
  9. PDF Salem T, Workman S, Jacobs N. 2020. Learning a Dynamic Map of Visual Appearance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR42600.2020.01245.
    bibtex | website
  10. PDF Workman S, Jacobs N. 2020. Dynamic Traffic Modeling from Overhead Imagery. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI: 10.1109/CVPR42600.2020.01233.
    bibtex | website
  11. Maretto RV, Fonseca LMG, Jacobs NB, Körting TS, Bendini HN, Parente LL. 2020. Spatio-Temporal Deep Learning Approach to Map Deforestation in Amazon Rainforest. IEEE Geoscience and Remote Sensing Letters 18:771–775. DOI: 10.1109/LGRS.2020.2986407.
    bibtex
  12. PDF Hamraz H, Jacobs NB, Contreras MA, Clark CH. 2019. Deep Learning for Conifer/Deciduous Classification of Airborne LiDAR 3D Point Clouds Representing Individual Trees. ISPRS Journal of Photogrammetry and Remote Sensing 158:219–230. DOI: 10.1016/j.isprsjprs.2019.10.011.
    bibtex
  13. PDF Jacobs N, Kraft A, Rafique MU, Sharma RD. 2018. A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). DOI: 10.1145/3274895.3274934.
    bibtex | code
  14. PDF Schulter S, Zhai M, Jacobs N, Chandraker M. 2018. Learning to Look around Objects for Top-View Representations of Outdoor Scenes. In: European Conference on Computer Vision (ECCV). DOI: 10.1007/978-3-030-01267-0_48.
    bibtex
  15. PDF Zhai M, Salem T, Greenwell C, Workman S, Pless R, Jacobs N. 2018. Learning Geo-Temporal Image Features. In: British Machine Vision Conference (BMVC).
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  16. PDF Song W, Workman S, Hadzic A, Souleyrette R, Green E, Chen M, Zhang X, Jacobs N. 2018. FARSA: Fully Automated Roadway Safety Assessment. In: IEEE Winter Conference on Applications of Computer Vision (WACV). DOI: 10.1109/WACV.2018.00063.
    bibtex
  17. PDF Workman S, Zhai M, Crandall D, Jacobs N. 2017. A Unified Model for Near and Remote Sensing. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.293.
    bibtex | website
  18. PDF Workman S, Souvenir R, Jacobs N. 2017. Understanding and Mapping Natural Beauty. In: IEEE International Conference on Computer Vision (ICCV). DOI: 10.1109/ICCV.2017.596.
    bibtex | website
  19. PDF 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 | code
  20. PDF Workman S, Zhai M, Jacobs N. 2016. Horizon Lines in the Wild. In: British Machine Vision Conference (BMVC).
    bibtex | website | code
  21. PDF 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.
    bibtex | code
  22. PDF 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 | website | code
  23. PDF Murdock C, Jacobs N, Pless R. 2015. Building Dynamic Cloud Maps from the Ground Up. In: IEEE International Conference on Computer Vision (ICCV). 1–9. DOI: 10.1109/ICCV.2015.85.
    bibtex
  24. PDF Islam MT, Greenwell C, Souvenir R, Jacobs N. 2015. Large-Scale Geo-Facial Image Analysis. EURASIP Journal on Image and Video Processing (JIVP) 2015:1–14. DOI: 10.1186/s13640-015-0070-9.
    bibtex | website
  25. Workman S, Souvenir R, Jacobs N. 2015. Scene Shape Estimation from Multiple Partly Cloudy Days. Computer Vision and Image Understanding (CVIU):116–129. DOI: 10.1016/j.cviu.2014.10.002.
    bibtex | website
  26. PDF Workman S, Mihail RP, Jacobs N. 2014. A Pot of Gold: Rainbows as a Calibration Cue. In: European Conference on Computer Vision (ECCV). 820–835. DOI: 10.1007/978-3-319-10602-1_53.
    bibtex | website
  27. PDF Jacobs N, Islam MT, Workman S. 2013. Cloud Motion as a Calibration Cue. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1344–1351. DOI: 10.1109/CVPR.2013.177.
    bibtex | website
  28. PDF Abrams A, Tucek J, Jacobs N, Pless R. 2012. LOST: Longterm Observation of Scenes (with Tracks). In: IEEE Workshop on Applications of Computer Vision (WACV). 297–304. DOI: 10.1109/WACV.2012.6163032.
    bibtex
  29. PDF Dixon M, Abrams A, Jacobs N, Pless R. 2011. On Analyzing Video with Very Small Motions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1–8. DOI: 10.1109/CVPR.2011.5995703.
    bibtex
  30. PDF Jacobs N, Bies B, Pless R. 2010. Using Cloud Shadows to Infer Scene Structure and Camera Calibration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1102–1109. DOI: 10.1109/CVPR.2010.5540093.
    bibtex | website
  31. PDF Jacobs N, Burgin W, Fridrich N, Abrams A, Miskell K, Braswell BH, Richardson AD, Pless R. 2009. The Global Network of Outdoor Webcams: Properties and Applications. In: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL). 111–120. DOI: 10.1145/1653771.1653789.
    bibtex
  32. PDF Jacobs N, Satkin S, Roman N, Speyer R, Pless R. 2007. Geolocating Static Cameras. In: IEEE International Conference on Computer Vision (ICCV). 1–6. DOI: 10.1109/ICCV.2007.4408995.
    bibtex | website
  33. PDF Jacobs N, Roman N, Pless R. 2007. Consistent Temporal Variations in Many Outdoor Scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1–6. DOI: 10.1109/CVPR.2007.383258.
    bibtex | website