CVPR 2017 – Wrap Up

It was quite exciting to attend the largest CVPR ever – almost 5000 attendees.  Having it in a beautiful location made it even more appealing.

Thanks to my students and colleagues who made the work we presented at CVPR possible.

Joint work with Imperial College and MIT on using copula models for joint facial AU estimation.
Joint NTU Singapore – Rutgers work on generative models for robust 3D face pose estimation
Break time at Waikiki beach
Hai presenting his work at the 1st Int’l Workshop on Deep Affective Learning and Context Modeling

ICCV 2017

Our paper about unsupervised probabilistic domain adaptation [1] for deep models (and other models too) has been accepted for ICCV’17:

[1] B. Gholami, O. Rudovic, and V. Pavlovic, “PUnDA: Probabilistic Unsupervised Domain Adaptation,” in Proc. IEEE International Conference Computer Vision, 2017.
[Bibtex]
@inproceedings{behnam17iccv,
author = {Gholami, Behnam and Rudovic, Ognjen and Pavlovic, Vladimir},
booktitle = {Proc. IEEE International Conference Computer Vision},
interhash = {4a8c0ac9cfbd7b02e2d86ef7b5a289a0},
intrahash = {9fd2aed9b960e71bf2472b5d70079060},
owner = {vladimir},
title = {PUnDA: Probabilistic Unsupervised Domain Adaptation},
year = 2017
}

Congratulations to Behnam and Ognjen!

Depth Recovery Paper

Our article on depth recovery using deformable object priors was accepted for publication in Journal of Visual Communication and Image Representation [1]:

[1] C. Chen, H. X. Pham, V. Pavlovic, J. Cai, G. Shi, and Y. Gao, “Using 3D Face Priors for Depth Recovery,” J. Visual Commun. Image Represent., 2017.
[Bibtex]
@article{chen17jvcir,
author = {Chen, Chongyu and Pham, Hai Xuan and Pavlovic, Vladimir and Cai, Jianfei and Shi, Guangming and Gao, Yuefang},
interhash = {14284536d8e03504dfb06b171c8598fe},
intrahash = {d52164653eb6d2d7324656f9cbc20d3f},
journal = {J. Visual Commun. Image Represent.},
owner = {vladimir},
title = {Using 3D Face Priors for Depth Recovery},
year = 2017
}

 

Congratulations!

CVPR 2017

 We are excited to have three CVPR 2017 main conference papers accepted [1, 2, 3], as well as one workshop paper [4] :

[1] B. Babagholami and V. Pavlovic, “Probabilistic Temporal Subspace Clustering,” in IEEE Int’l Conf. Computer Vision and Pattern Recognition, 2017.
[Bibtex]
@inproceedings{babagholami17cvpr,
author = {Babagholami, Behnam and Pavlovic, Vladimir},
booktitle = {IEEE Int'l Conf. Computer Vision and Pattern Recognition},
date-added = {2017-01-17 15:43:46 +0000},
date-modified = {2017-01-17 15:45:08 +0000},
interhash = {92906adf52c283061c4f06eaac54aeca},
intrahash = {e0a04837c7cd0540d2cd2f0aa442a097},
note = {Under review. 50\% contribution.},
title = {Probabilistic Temporal Subspace Clustering},
year = 2017
}
[2] R. Walecki, O. Rudovic, V. Pavlovic, B. Schuller, and M. Pantic, “Deep Structured Learning for Facial Expression Intensity Estimation,” in IEEE Int’l Conf. Computer Vision and Pattern Recognition, 2017.
[Bibtex]
@inproceedings{walecki17cvpr,
author = {Walecki, Robert and Rudovic, Ognjen and Pavlovic, Vladimir and Schuller, Bjorn and Pantic, Maja},
booktitle = {IEEE Int'l Conf. Computer Vision and Pattern Recognition},
date-added = {2017-01-17 15:41:28 +0000},
date-modified = {2017-01-17 15:43:23 +0000},
interhash = {956179029a34e1c71fc4b94fb1a90ee8},
intrahash = {fd5d11a30fb9f63aa76cfe46783b9bd8},
title = {Deep Structured Learning for Facial Expression Intensity Estimation},
year = 2017
}
[3] L. Sheng, J. Cai, T. Cham, V. Pavlovic, and K. N. Ngan, “A Generative Model for Depth-based Robust 3D Facial Pose Tracking,” in IEEE Int’l Conf. Computer Vision and Pattern Recognition, 2017.
[Bibtex]
@inproceedings{sheng17cvpr,
author = {Sheng, Lu and Cai, Jianfei and Cham, Tat-Jen and Pavlovic, Vladimir and Ngan, King Ngi},
booktitle = {IEEE Int'l Conf. Computer Vision and Pattern Recognition},
date-added = {2017-01-17 15:45:33 +0000},
date-modified = {2017-01-17 15:46:47 +0000},
interhash = {112c6e22dda03c3d485b79595d210af1},
intrahash = {e18c6335e20344d4a11bceb8ec657b57},
note = {Under review. 25\% contribution.},
title = {A Generative Model for Depth-based Robust 3D Facial Pose Tracking},
year = 2017
}
[4] H. Pham and V. Pavlovic, “Speech-driven 3D Facial Animation with Implicit Emotional Awareness: A Deep Learning Approach,” in IEEE Int’l Conf. Computer Vision and Pattern Recognition – Workshop on Deep Affective Learning and Context Modeling, 2017.
[Bibtex]
@inproceedings{pham17cvpr,
author = {Pham, Hai and Pavlovic, Vladimir},
booktitle = {IEEE Int'l Conf. Computer Vision and Pattern Recognition - Workshop on Deep Affective Learning and Context Modeling},
interhash = {538627e583e79fae93dd8af09fcdc7eb},
intrahash = {e6c72a93e19dd9fe1270b6d559534637},
owner = {vladimir},
title = {Speech-driven 3D Facial Animation with Implicit Emotional Awareness: A Deep Learning Approach},
year = 2017
}

 

Go to our Research page to find out more.

Congratulations to all!

Ancient Coin Recognition Paper

 Our article dealing with the problem of Ancient Coin Recognition was recently accepted for publication [1]:

[1] [doi] J. Kim and V. Pavlovic, “Discovering Characteristic Landmarks on Ancient Coins Using Convolutional Networks,” SPIE Journal of Electronic Imaging, 2017.
[Bibtex]
@article{jongpil16spie,
author = {Kim, Jongpil and Pavlovic, Vladimir},
date-added = {2016-09-11 21:37:27 +0000},
date-modified = {2017-01-17 15:49:38 +0000},
doi = {10.1117/1.JEI.26.1.011018},
interhash = {0b5ba506800f82bb385df4391c8290d9},
intrahash = {0a2ffe339e53fea346b16501a76c5844},
journal = {{SPIE} Journal of Electronic Imaging},
note = {Accepted for publication. 50\% contribution.},
title = {Discovering Characteristic Landmarks on Ancient Coins Using Convolutional Networks},
year = 2017
}

Congratulations to Jongpil!