Prof. Pavlovic is the director of SEQAM Lab. He earned the Ph.D. in electrical engineering at the University of Illinois at Urbana-Champaign in 1999. He was a research scientist at Compaq’s Cambridge Research Lab in Cambridge, MA from 1999 until 2001. In 2001 he joined Boston University as a research faculty in the Bioinformatics Program. In 2002 he joined Rutgers as an Assistant Professor in the Department of Computer Science, where he is now a Full Professor. Prof. Pavlovic’s research interests include statistical learning, dynamic system modeling and computer vision.
He is currently on leave from Rutgers, leading the Digital Gastronomy Lab (DGL) at Samsung’s AI Center in Cambridge, UK, as Samsung Principal Scientist. DGL focuses on revolutionazing the fields of nutrition, food, and wellness using state-of-the-art AI.
2020
- F. Han, R. Guerrero, and V. Pavlovic, “CookGAN: Meal Image Synthesis from Ingredients,” in Winter Conference on Applications of Computer Vision (WACV ’20), Aspen, Colorado, 2020.
[BibTeX]@InProceedings{han20wacv, author = {Fangda Han and Ricardo Guerrero and Vladimir Pavlovic}, booktitle = {Winter Conference on Applications of Computer Vision ({WACV} ’20)}, title = {{CookGAN}: Meal Image Synthesis from Ingredients}, year = {2020}, address = {Aspen, Colorado}, month = mar, date-added = {2019-09-09 15:01:10 -0400}, date-modified = {2019-09-09 15:02:39 -0400}, }
2019
- S. S. Sohn, S. Moon, H. Zhou, S. Yoon, V. Pavlovic, and M. Kapadia, “Deep Crowd-Flow Prediction in Built Environments,” in Neural Information Processing Systems NeurIPS, Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response, Montreal, Canada, 2019.
[BibTeX]@InProceedings{son19nipsws, author = {Samuel S. Sohn and Seonghyeon Moon and Honglu Zhou and Sejong Yoon and Vladimir Pavlovic and Mubbasir Kapadia}, booktitle = {Neural Information Processing Systems {NeurIPS}, Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response}, title = {Deep Crowd-Flow Prediction in Built Environments}, year = {2019}, address = {Montreal, Canada}, month = dec, date-added = {2019-09-09 15:01:10 -0400}, date-modified = {2019-09-09 15:02:39 -0400}, } - M. Lee, O. Rudovic, V. Pavlovic, and M. Pantic, “Fast Adaptation of Personalized Deep Learning for Facial Action Unit Detection,” in Int’l Joint Conference on Artificial Intelligence IJCNN, Workshop on Affective Computing, Macao, China, 2019.
[BibTeX]@InProceedings{lee19ijcaiws, author = {Mihee Lee and Ognjen Rudovic and Vladimir Pavlovic and Maja Pantic}, booktitle = {Int'l Joint Conference on Artificial Intelligence {IJCNN}, Workshop on Affective Computing}, title = {Fast Adaptation of Personalized Deep Learning for Facial Action Unit Detection}, year = {2019}, address = {Macao, China}, month = aug, date-added = {2019-09-09 15:01:10 -0400}, date-modified = {2019-09-09 15:02:39 -0400}, } - M. Kim, Y. Wang, P. Sahu, and V. Pavlovic, “Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement,” in IEEE International Conference on Computer Vision, ICCV, Seoul, Korea, 2019.
[BibTeX]@InProceedings{kim19iccv, author = {Minyoung Kim and Yuting Wang and Pritish Sahu and Vladimir Pavlovic}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV}}, title = {Bayes-Factor-{VAE}: Hierarchical {Bayesian} Deep Auto-Encoder Models for Factor Disentanglement}, year = {2019}, address = {Seoul, Korea}, month = oct, date-added = {2019-09-05 20:58:51 +0100}, date-modified = {2019-09-05 21:00:05 +0100}, } - B. Gholami, P. Sahu, M. Kim, and V. Pavlovic, “Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation,” in IEEE International Conference on Computer Vision, ICCV, 6th Workshop on Transferring and Adapting Source Knowledge in Computer Vision, Seoul, Korea, 2019.
[BibTeX]@InProceedings{gholami19iccvws, author = {Behnam Gholami and Pritish Sahu and Minyoung Kim and Vladimir Pavlovic}, booktitle = {{IEEE} International Conference on Computer Vision, {ICCV}, 6th Workshop on Transferring and Adapting Source Knowledge in Computer Vision}, title = {Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation}, year = {2019}, address = {Seoul, Korea}, month = oct, date-added = {2019-09-05 20:54:25 +0100}, date-modified = {2019-09-05 20:57:09 +0100}, } - J. Li, R. Guerrero, and V. Pavlovic, “Deep Cooking: Predicting Relative Food Ingredient Amounts from Images,” in 5th International Workshop on Multimedia Assisted Dietary Management (MADiMa ’19), Nice, France, 2019. doi:10.1145/3347448.3357164
[BibTeX]@InProceedings{li19madima, author = {Jiatong Li and Ricardo Guerrero and Vladimir Pavlovic}, booktitle = {5th International Workshop on Multimedia Assisted Dietary Management ({MADiMa} '19)}, title = {Deep Cooking: Predicting Relative Food Ingredient Amounts from Images}, year = {2019}, address = {Nice, France}, month = oct, date-added = {2019-09-05 20:51:44 +0100}, date-modified = {2019-09-05 20:52:48 +0100}, doi = {10.1145/3347448.3357164}, } - J. Li, R. Guerrero, and V. Pavlovic, “Deep Cooking: Predicting Food Ingredient Amounts from Images,” in Int’l Joint Conference on Artificial Intelligence IJCNN, Workshop on AI and Food, Macao, China, 2019.
[BibTeX]@InProceedings{li19ijcai, author = {Jiatong Li and Ricardo Guerrero and Vladimir Pavlovic}, booktitle = {Int'l Joint Conference on Artificial Intelligence {IJCNN}, Workshop on AI and Food}, title = {Deep Cooking: Predicting Food Ingredient Amounts from Images}, year = {2019}, address = {Macao, China}, month = aug, date-added = {2019-09-05 20:49:20 +0100}, date-modified = {2019-09-05 20:50:24 +0100}, } - G. Qiao, H. Zhou, S. Yoon, M. Kapadia, and V. Pavlovic, “Scenario Generalization of Data-driven Imitation Models in Crowd Simulation,” in ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG), 2019. doi:10.1145/3359566.3360087
[BibTeX]@InProceedings{gang19mig, author = {Gang Qiao and Honglu Zhou and Sejong Yoon and Mubbasir Kapadia and Vladimir Pavlovic}, booktitle = {ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG)}, title = {Scenario Generalization of Data-driven Imitation Models in Crowd Simulation}, year = {2019}, date-added = {2019-09-05 20:43:26 +0100}, date-modified = {2019-09-05 20:46:44 +0100}, doi = {10.1145/3359566.3360087}, } - L. Zhao, F. Han, X. Peng, X. Zhang, M. Kapadia, V. Pavlovic, and D. N. Metaxas, “Cartoonish sketch-based face editing in videos using identity deformation transfer,” Computers & Graphics, vol. 79, p. 58–68, 2019. doi:10.1016/j.cag.2019.01.004
[BibTeX]@Article{ZhaoH0ZKPM19, author = {Long Zhao and Fangda Han and Xi Peng and Xun Zhang and Mubbasir Kapadia and Vladimir Pavlovic and Dimitris N. Metaxas}, journal = {Computers {\&} Graphics}, title = {Cartoonish sketch-based face editing in videos using identity deformation transfer}, year = {2019}, pages = {58--68}, volume = {79}, doi = {10.1016/j.cag.2019.01.004}, } - L. Sheng, J. Cai, T. -, V. Pavlovic, and K. N. Ngan, “Visibility Constrained Generative Model for Depth-Based 3D Facial Pose Tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, iss. 8, p. 1994–2007, 2019. doi:10.1109/tpami.2018.2877675
[BibTeX]@Article{ShengCCPN19, author = {Lu Sheng and Jianfei Cai and Tat{-}Jen Cham and Vladimir Pavlovic and King Ngi Ngan}, journal = {{IEEE} Trans. Pattern Anal. Mach. Intell.}, title = {Visibility Constrained Generative Model for Depth-Based 3D Facial Pose Tracking}, year = {2019}, number = {8}, pages = {1994--2007}, volume = {41}, doi = {10.1109/tpami.2018.2877675}, } - M. Kim, Y. Wang, P. Sahu, and V. Pavlovic, “Relevance Factor VAE: Learning and Identifying Disentangled Factors,” CoRR, vol. abs/1902.01568, 2019.
[BibTeX]@article{kim19rfvae_arxiv, Author = {Minyoung Kim and Yuting Wang and Pritish Sahu and Vladimir Pavlovic}, Journal = {CoRR}, Title = {Relevance Factor {VAE:} Learning and Identifying Disentangled Factors}, Volume = {abs/1902.01568}, Year = {2019}} - M. Kim, P. Sahu, B. Gholami, and V. Pavlovic, “Unsupervised visual domain adaptation: A deep max-margin Gaussian process approach,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, p. 4380–4390.
[BibTeX]@inproceedings{kim2019ugpda, Author = {Kim, Minyoung and Sahu, Pritish and Gholami, Behnam and Pavlovic, Vladimir}, Booktitle = {Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, Pages = {4380--4390}, Title = {Unsupervised visual domain adaptation: A deep max-margin {Gaussian} process approach}, Year = {2019}} - M. Kim, P. Sahu, B. Gholami, and V. Pavlovic, “Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach,” CoRR, vol. abs/1902.08727, 2019.
[BibTeX]@article{kim19ugpda_arxiv, Author = {Minyoung Kim and Pritish Sahu and Behnam Gholami and Vladimir Pavlovic}, Journal = {CoRR}, Title = {Unsupervised Visual Domain Adaptation: {A} Deep Max-Margin {Gaussian} Process Approach}, Volume = {abs/1902.08727}, Year = {2019}} - L. Sheng, J. Cai, T. -, V. Pavlovic, and K. N. Ngan, “Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking,” CoRR, vol. abs/1905.02114, 2019. doi:10.1109/tpami.2018.2877675
[BibTeX]@Article{sheng19vcgm_arxiv, author = {Lu Sheng and Jianfei Cai and Tat{-}Jen Cham and Vladimir Pavlovic and King Ngi Ngan}, journal = {CoRR}, title = {Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking}, year = {2019}, volume = {abs/1905.02114}, doi = {10.1109/tpami.2018.2877675}, } - I. H. Laradji, M. Schmidt, V. Pavlovic, and M. Kim, “Efficient Deep Gaussian Process Models for Variable-Sized Input,” in Int’l Joint Conference on Neural Networks IJCNN, Budapest, Hungary, 2019. doi:10.1109/ijcnn.2019.8851768
[BibTeX]@InProceedings{laradji19dgpvsi, author = {Issam H. Laradji and Mark Schmidt and Vladimir Pavlovic and Minyoung Kim}, booktitle = {Int'l Joint Conference on Neural Networks {IJCNN}}, title = {Efficient Deep Gaussian Process Models for Variable-Sized Input}, year = {2019}, address = {Budapest, Hungary}, month = jul, date-modified = {2019-09-05 21:26:55 +0100}, doi = {10.1109/ijcnn.2019.8851768}, } - I. H. Laradji, M. Schmidt, V. Pavlovic, and M. Kim, “Efficient Deep Gaussian Process Models for Variable-Sized Input,” CoRR, vol. abs/1905.06982, 2019. doi:10.1109/ijcnn.2019.8851768
[BibTeX]@Article{laradji19dgpvsi_arxiv, author = {Issam H. Laradji and Mark Schmidt and Vladimir Pavlovic and Minyoung Kim}, journal = {CoRR}, title = {Efficient Deep Gaussian Process Models for Variable-Sized Input}, year = {2019}, volume = {abs/1905.06982}, doi = {10.1109/ijcnn.2019.8851768}, } - F. Han, R. Guerrero, and V. Pavlovic, “VirtualCook: Cross-modal Synthesis of Food Images from Ingredients,” in Int’l Joint Conference on Artificial Intelligence IJCNN, Workshop on AI and Food, Macao, China, 2019.
[BibTeX]@inproceedings{han19art_ijcai, Address = {Macao, China}, Author = {Fangda Han and Ricardo Guerrero and Vladimir Pavlovic}, Booktitle = {Int'l Joint Conference on Artificial Intelligence {IJCNN}, Workshop on AI and Food}, Date-Modified = {2019-09-05 20:50:46 +0100}, Month = aug, Title = {VirtualCook: Cross-modal Synthesis of Food Images from Ingredients}, Year = {2019}} - F. Han, R. Guerrero, and V. Pavlovic, “The Art of Food: Meal Image Synthesis from Ingredients,” CoRR, vol. abs/1905.13149, 2019.
[BibTeX]@Article{han19art_arxiv, author = {Fangda Han and Ricardo Guerrero and Vladimir Pavlovic}, journal = {CoRR}, title = {The Art of Food: Meal Image Synthesis from Ingredients}, year = {2019}, volume = {abs/1905.13149}, eprint = {1905.13149v1}, }
2018
- G. Qiao, S. Yoon, M. Kapadia, and V. Pavlovic, “The Role of Data-Driven Priors in Multi-Agent Crowd Trajectory Estimation,” in AAAI, 2018, p. 4710–4717.
[BibTeX]@inproceedings{gang19priors, Author = {Gang Qiao and Sejong Yoon and Mubbasir Kapadia and Vladimir Pavlovic}, Booktitle = {{AAAI}}, Pages = {4710--4717}, Publisher = {{AAAI} Press}, Title = {The Role of Data-Driven Priors in Multi-Agent Crowd Trajectory Estimation}, Year = {2018}} - H. X. Pham, Y. Wang, and V. Pavlovic, “End-to-end Learning for 3D Facial Animation from Speech,” in ICMI, 2018, p. 361–365. doi:10.1145/3242969.3243017
[BibTeX]@InProceedings{hai18icmi, author = {Hai Xuan Pham and Yuting Wang and Vladimir Pavlovic}, booktitle = {{ICMI}}, title = {End-to-end Learning for 3D Facial Animation from Speech}, year = {2018}, pages = {361--365}, publisher = {{ACM}}, doi = {10.1145/3242969.3243017}, }