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!