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.

Follow @multimodal_lab

recent news

• 22 Aug 2023 : two papers accepted to BMVC, congrats to lab members: Subash Khanal, Zhexiao Xiong, Aayush Dhakal, Srikumar Sastry, and Yu Zhang
• 25 Jul 2023 : congrats to Alex Wollam on successfully defending his MS project
• 28 Apr 2023 : congrats to Alex Greene on successfully defending his MS project
• 05 Apr 2023 : six papers accepted to IGARSS, congrats to lab members: Aayush Dhakal, Aram Ansary Ogholbake, Benjamin Brodie, Cohen Archbold, Jacob Birge, M. Usman Rafique, Raian Vargas Maretto, Srikumar Sastry, Subash Khanal, and Yu Zhang
• 20 Feb 2023 : congrats to Yu Zhang on successfully defending his dissertation
• 15 Jan 2023 : welcome to new postdoc Adeel Ahmad, PhD student Lydia Reader, and MS student Alex Wollam!

Check the archives for old news.

selected recent publications

See our publications page for a complete listing.

1. 

   Xiong Z, Qiao F, Zhang Y, Jacobs N. 2023. StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation. In: British Machine Vision Conference (BMVC).
   bibtex

   close  

   @inproceedings{xiong2023stereo,
     title = {StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation},
     author = {Xiong, Zhexiao and Qiao, Feng and Zhang, Yu and Jacobs, Nathan},
     year = {2023},
     booktitle = {British Machine Vision Conference (BMVC)},
     author+an = {4=highlight},
     pdf = {https://arxiv.org/pdf/2309.01842.pdf},
     annotation = {calibration,localization,career,spotlight}
   }
   

2. 

   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). DOI: 10.1109/CVPR52688.2022.00182.
   bibtex

   close  

   @inproceedings{workman2022nearremote,
     annotation = {spotlight,remote_sensing,social_media},
     author = {Workman, Scott and Rafique, Muhammad Usman and Blanton, Hunter and Jacobs, Nathan},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     thumbnail = {/thumbnails/nearremote2.jpg},
     title = {Revisiting Near/Remote Sensing with Geospatial Attention},
     pdf = {https://arxiv.org/pdf/2204.01807},
     doi = {10.1109/CVPR52688.2022.00182},
     tweet = {https://twitter.com/multimodal_lab/status/1511779656684343299},
     note = {Acceptance rate: 25.33\%},
     month = jun,
     year = {2022}
   }
   

3. 

   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

   close  

   @article{padilha2022contentbased,
     title = {Content-Based Detection of Temporal Metadata Manipulation},
     author = {Padilha, Rafael and Salem, Tawfiq and Workman, Scott and Andaló, Fernanda A. and Rocha, Anderson and Jacobs, Nathan},
     year = {2022},
     month = mar,
     day = {11},
     thumbnail = {/thumbnails/tampering.jpg},
     doi = {10.1109/TIFS.2022.3159154},
     journal = {IEEE Transactions on Information Forensics and Security},
     author+an = {6=highlight},
     pdf = {https://arxiv.org/pdf/2103.04736},
     pages = {1316--1327},
     project = {https://rafaspadilha.github.io/publication/padilha22content/},
     tweet = {https://twitter.com/jacobsn/status/1498729825523015681},
     annotation = {spotlight,webcam,calibration,skyfinder,localization,career,social_media,remote_sensing}
   }
   

4. 

   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). DOI: 10.1109/WACV51458.2022.00028.
   bibtex

   close  

   @inproceedings{blanton2022structureaware,
     title = {A Structure-Aware Method for Direct Pose Estimation},
     author = {Blanton, Hunter and Workman, Scott and Jacobs, Nathan},
     year = {2022},
     doi = {10.1109/WACV51458.2022.00028},
     month = jan,
     booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
     author+an = {3=highlight},
     pdf = {https://arxiv.org/pdf/2012.12360},
     annotation = {calibration,localization,career,spotlight},
     thumbnail = {/thumbnails/direct_pose.jpg},
     primaryclass = {cs.CV}
   }
   

5. 

   Zhang Y, Liang G, Jacobs N. 2021. Dynamic Feature Alignment for Semi-supervised Domain Adaptation. In: British Machine Vision Conference (BMVC).
   bibtex

   close  

   @inproceedings{zhang2021dfa,
     title = {Dynamic Feature Alignment for Semi-supervised Domain Adaptation},
     author = {Zhang, Yu and Liang, Gongbo and Jacobs, Nathan},
     year = {2021},
     month = nov,
     booktitle = {British Machine Vision Conference (BMVC)},
     author+an = {3=highlight},
     annotation = {spotlight},
     pdf = {https://arxiv.org/pdf/2110.09641},
     thumbnail = {/thumbnails/dfa.jpg}
   }
   

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