Yuting Wang

Welcome to my homepage. I am a graduate student at Department of Computer Science, School of Arts and Sciences, at Rutgers University advised by Professor Vladimir Pavlovic. I did a recent internship with Maja Pantic and Vladimir Pavlovic at Samsung Research AI Center (SRUK). Previously, I got my Master’s degree at Karlsruhe Institute of Technology advised by Professor Alexander Waibel and Professor Florian Metze, and received my B.E. at University of Duisburg-Essen, both with highest honor.

My research interests include computer vision, speech recognition, cognitive system, machine learning and deep learning.

Vita

Current Research Topics

Representation Learning, Automatic Facial Animation, Attention Model

Research Project:

  • Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement.
[Bibtex]

@inproceedings{kim19iccv,
Address = {Seoul, Korea},
Author = {Minyoung Kim and Yuting Wang and Pritish Sahu and Vladimir Pavlovic},
Booktitle = {{IEEE} International Conference on Computer Vision, {ICCV}},
Date-Added = {2019-09-05 20:58:51 +0100},
Date-Modified = {2019-09-05 21:00:05 +0100},
Month = {October},
Title = {Bayes-Factor-{VAE}: Hierarchical {Bayesian} Deep Auto-Encoder Models for Factor Disentanglement},,
Year = {2019}
}

  • Relevance Factor VAE: Learning and Identifying Disentangled Factors.
[Bibtex]

@article{kim2019relevance,
title = {Relevance Factor VAE: Learning and Identifying Disentangled Factors},
author = {Kim, Minyoung and Wang, Yuting and Sahu, Pritish and Pavlovic, Vladimir},
journal={arXiv preprint arXiv:1902.01568},
year = {2019}
}

  • A Hybrid Generative Adversarial Model for Collaborative Filtering
  • End-to-End Learning for 3D Facial Animation from Speech.
[Bibtex]

@inproceedings{Pham:2018:ELF:3242969.3243017,
author = {Pham, Hai Xuan and Wang, Yuting and Pavlovic, Vladimir},
title = {End-to-end Learning for 3D Facial Animation from Speech},
booktitle = {Proceedings of the 20th ACM International Conference on Multimodal Interaction},
series = {ICMI ’18},
year = {2018},
isbn = {978-1-4503-5692-3},
location = {Boulder, CO, USA},
pages = {361–365},
numpages = {5},
url = {http://doi.acm.org/10.1145/3242969.3243017},
doi = {10.1145/3242969.3243017},
acmid = {3243017},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deep learning, facial animation, speech-driven},
}

  • Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network.
[Bibtex]

article{pham2018generative,
title = {Generative adversarial talking head: Bringing portraits to life with a weakly supervised neural network},
author = {Pham, Hai X and Wang, Yuting and Pavlovic, Vladimir},
journal={arXiv preprint arXiv:1803.07716},
year = {2018}
}

  • End-to-end Learning from Sketch to Facial Expression

Publications:

Authors: Type:

2025

  • X. Zhang, S. Wen, L. Han, F. Juefei, A. Srivastava, J. Huang, H. Wang, M. Tao, V. Pavlovic, and D. Metaxas, “SODA: Spectral Orthogonal Decomposition Adaptation for Diffusion Models,” in IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025, 2025.
    [BibTeX] [Download PDF] 
    @inproceedings{zhang25wacv,
author = {Xinxi Zhang and Song Wen and Ligong Han and Felix Juefei and Akash Srivastava and Junzhou Huang and Hao Wang and Molei Tao and Vladimir Pavlovic and Dimitris Metaxas},
title = {{SODA}: Spectral Orthogonal Decomposition Adaptation for Diffusion Models},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision, {WACV} 2025},
month = {February},
year = {2025},
url = {https://openaccess.thecvf.com/content/WACV2025/papers/Zhang_SODA_Spectral_Orthogonal_Decomposition_Adaptation_for_Diffusion_Models_WACV_2025_paper.pdf}
}
  • K. Baisoti, P. Sahu, T. Q. Liu, Z. Xu, H. Wang, and V. Pavlovic, “GenVP: Generating Visual Puzzles With Contrastive Hierarchical VAEs,” in 13h International Conference on Learning Representations, ICLR 2025, 2025.
    [BibTeX] [Download PDF] 
    @inproceedings{basioti25iclr,
author = {Kalliopi Baisoti and Pritish Sahu and Tony Qingze Liu and Zihao Xu and Hao Wang and Vladimir Pavlovic},
title = {{GenVP}: Generating Visual Puzzles With Contrastive Hierarchical {VAE}s},
booktitle = {13h International Conference on Learning Representations, {ICLR} 2025},
month = {April},
year = {2025},
url = {https://arxiv.org/pdf/2503.23598}
}

2024

  • K. Basioti, P. Sahu, T. Q. Liu, Z. Xu, H. Wang, and V. Pavlovic, “Generative Visual Puzzles with Global-Local Masked Contrastive HVAEs,” , 2024.
    [BibTeX] 
    @article{basioti24neurips,
author = {Kalliopi Basioti and Pritish Sahu and Tony Qingze Liu and Zihao Xu and Hao Wang and Vladimir Pavlovic},
title = {Generative Visual Puzzles with Global-Local Masked Contrastive {HVAE}s},
journal = {},
year = {2024},
note = {Under review}
}

2023

  • Y. Wang, V. Ilic, J. Li, B. Kisavcanin, and V. Pavlovic, “ALWOD: Active Learning for Weakly-Supervised Object Detection,” in IEEE International Conference on Computer Vision, ICCV, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{Wang2023ALWODAL,
title = {ALWOD: Active Learning for Weakly-Supervised Object Detection},
author = {Yuting Wang and Velibor Ilic and Jiatong Li and Branislav Kisa\vcanin and Vladimir Pavlovic},
booktitle = {{IEEE} International Conference on Computer Vision, {ICCV}},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:261822475}
}
  • J. Wang, D. Massiceti, X. Hu, V. Pavlovic, and T. Lukasiewicz, “NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation,” in ICML 2023, 2023.
    [BibTeX] [Download PDF] 
    @inproceedings{wang2023np-semiseg,
author = {Wang, Jianfeng and Massiceti, Daniela and Hu, Xiaolin and Pavlovic, Vladimir and Lukasiewicz, Thomas},
title = {NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation},
booktitle = {ICML 2023},
year = {2023},
month = {June},
url = {https://www.microsoft.com/en-us/research/publication/np-semiseg-when-neural-processes-meet-semi-supervised-semantic-segmentation/}
}
  • Y. Wang, R. Guerrero, and V. Pavlovic, “D\(^\mbox2\)F2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation,” in IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023, Waikoloa, HI, USA, January 2-7, 2023, 2023, p. 22–31. doi:10.1109/WACV56688.2023.00011
    [BibTeX] [Download PDF] 
    @inproceedings{WangGP23,
author = {Yuting Wang and Ricardo Guerrero and Vladimir Pavlovic},
title = {D\({}^{\mbox{2}}\)F2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation},
booktitle = {{IEEE/CVF} Winter Conference on Applications of Computer Vision, {WACV} 2023, Waikoloa, HI, USA, January 2-7, 2023},
pages = {22--31},
publisher = {{IEEE}},
year = {2023},
url = {https://doi.org/10.1109/WACV56688.2023.00011},
doi = {10.1109/WACV56688.2023.00011},
timestamp = {Tue, 14 Feb 2023 22:26:55 +0100},
biburl = {https://dblp.org/rec/conf/wacv/WangGP23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

2022

  • H. X. Pham, Y. Wang, and V. Pavlovic, “Learning Continuous Facial Actions From Speech for Real-Time Animation,” IEEE Trans. Affect. Comput., vol. 13, iss. 3, p. 1567–1580, 2022. doi:10.1109/TAFFC.2020.3022017
    [BibTeX] [Download PDF] 
    @article{PhamWP22,
author = {Hai Xuan Pham and Yuting Wang and Vladimir Pavlovic},
title = {Learning Continuous Facial Actions From Speech for Real-Time Animation},
journal = {{IEEE} Trans. Affect. Comput.},
volume = {13},
number = {3},
pages = {1567--1580},
year = {2022},
url = {https://doi.org/10.1109/TAFFC.2020.3022017},
doi = {10.1109/TAFFC.2020.3022017},
timestamp = {Tue, 11 Oct 2022 14:59:07 +0200},
biburl = {https://dblp.org/rec/journals/taffco/PhamWP22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
  • J. Wang, T. Lukasiewicz, D. Massiceti, X. Hu, V. Pavlovic, and A. Neophytou, “NP-Match: When Neural Processes meet Semi-Supervised Learning,” in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, 2022, p. 22919–22934.
    [BibTeX] [Download PDF] 
    @inproceedings{WangLMHPN22,
author = {Jianfeng Wang and Thomas Lukasiewicz and Daniela Massiceti and Xiaolin Hu and Vladimir Pavlovic and Alexandros Neophytou},
editor = {Kamalika Chaudhuri and Stefanie Jegelka and Le Song and Csaba Szepesv{\'{a}}ri and Gang Niu and Sivan Sabato},
title = {NP-Match: When Neural Processes meet Semi-Supervised Learning},
booktitle = {International Conference on Machine Learning, {ICML} 2022, 17-23 July 2022, Baltimore, Maryland, {USA}},
series = {Proceedings of Machine Learning Research},
volume = {162},
pages = {22919--22934},
publisher = {{PMLR}},
year = {2022},
url = {https://proceedings.mlr.press/v162/wang22s.html},
timestamp = {Tue, 11 Oct 2022 14:59:07 +0200},
biburl = {https://dblp.org/rec/conf/icml/WangLMHPN22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
  • J. Wang, T. Lukasiewicz, D. Massiceti, X. Hu, V. Pavlovic, and A. Neophytou, “NP-Match: When Neural Processes meet Semi-Supervised Learning,” CoRR, vol. abs/2207.01066, 2022. doi:10.48550/arXiv.2207.01066
    [BibTeX] [Download PDF] 
    @article{wang22abs-2207-01066,
author = {Jianfeng Wang and Thomas Lukasiewicz and Daniela Massiceti and Xiaolin Hu and Vladimir Pavlovic and Alexandros Neophytou},
title = {NP-Match: When Neural Processes meet Semi-Supervised Learning},
journal = {CoRR},
volume = {abs/2207.01066},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2207.01066},
doi = {10.48550/arXiv.2207.01066},
eprinttype = {arXiv},
eprint = {2207.01066},
timestamp = {Tue, 11 Oct 2022 14:59:07 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2207-01066.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

2019

  • 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,
address = {Seoul, Korea},
author = {Minyoung Kim and Yuting Wang and Pritish Sahu and Vladimir Pavlovic},
booktitle = {{IEEE} International Conference on Computer Vision, {ICCV}},
date-added = {2019-09-05 20:58:51 +0100},
date-modified = {2019-09-05 21:00:05 +0100},
month = oct,
title = {Bayes-Factor-{VAE}: Hierarchical {Bayesian} Deep Auto-Encoder Models for Factor Disentanglement},
year = {2019}
}
  • 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}
}

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}},
doi = {10.1145/3242969.3243017},
pages = {361--365},
publisher = {{ACM}},
title = {End-to-end Learning for 3D Facial Animation from Speech},
year = {2018},
bdsk-url-1 = {https://doi.org/10.1145/3242969.3243017}
}
  • H. X. Pham, Y. Wang, and V. Pavlovic, “Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network,” CoRR, vol. abs/1803.07716, 2018.
    [BibTeX] 
    @article{hai18gath_arxiv,
author = {Hai Xuan Pham and Yuting Wang and Vladimir Pavlovic},
journal = {CoRR},
title = {Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network},
volume = {abs/1803.07716},
year = {2018}
}

2017

  • H. X. Pham, Y. Wang, and V. Pavlovic, End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech, 2017.
    [BibTeX] [Download PDF] 
    @unpublished{pham18fg,
author = {Hai Xuan Pham and Yuting Wang and Vladimir Pavlovic},
keywords = {emotions, face animation, speech},
note = {under review},
title = {End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech},
url = {https://arxiv.org/abs/1710.00920},
year = {2017},
bdsk-url-1 = {https://arxiv.org/abs/1710.00920}
}
  • H. X. Pham, Y. Wang, and V. Pavlovic, Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network.
    [BibTeX] 
    @unpublished{hai19tac,
author = {Hai Xuan Pham and Yuting Wang and Vladimir Pavlovic},
date-added = {2019-09-05 21:05:51 +0100},
date-modified = {2019-09-09 16:39:27 -0400},
note = {Under review},
title = {Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network}
}

Teaching Assistant

  • Introduction to Discrete Structures I (CS-205), Rutgers University, Instructor: Antonio Miranda, Fall 2017.
  • Introduction to Discrete Structures II (CS-206), Rutgers University, Instructor: Eliane Traldi, Spring 2018.
  • Data Structure (CS-112), Rutgers University, Instructor: Kevin Mc Inerney, Summer 2018.
  • Introduction to Discrete Structures I (CS-205), Rutgers University, Instructor: Wes Cowan, Fall 2018
  • Introduction to Artificial Intelligence (CS-440), Rutgers University, Instructor: Abdeslam Boularias, Fall 2019.
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