Sequence Analysis and Modeling Lab
This is a joint work by Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic
Our new paper on Domain Adaptation was accepted in Multi-Discipline Approach for Learning Concepts – Zero-Shot, One-Shot, Few-Shot, and Beyond and Beyond Workshop in conjunction with ICCV 2019.
This is a joint work by Behnam Gholami, Pritish Sahu, Ognjen Rudovic, Konstantinos Bousmalis, Vladimir Pavlovic
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings … Read the rest
Abstract
Detecting facial action units (AU) is one of the fundamental steps in automatic recognition of facial expression of emotions and cognitive states. Though there have been a variety of … Read the rest
Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic.
We propose a novel VAE-based deep auto- encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all … Read the rest
Hai X. Pham, Yuting Wang & Vladimir Pavlovic.
This paper presents Generative Adversarial Talking Head, a novel deep generative neural network that enables fully automatic facial expression synthesis of an arbitrary portrait with continuous action unit (AU) coefficients. … Read the rest
This is a joint work by Hai Xuan Pham, Yuting Wang, and Vladimir Pavlovic. The paper was accepted by the 20th ACM International Conference on Multimodal Interaction.
We present a deep learning framework for real-time speech-driven 3D facial animation … Read the rest
Our new paper on crowd simulation was accepted by ACM SIGGRAPH Conference on Motion, Interaction and Games 2019. Congratulations to Gang!
ABSTRACT
Crowd simulation, the study of the movement of multiple agents in complex environments, presents a unique application domain for … Read the rest
Our new paper on Bayesian representation learning was accepted as Oral at ICCV 2019. Congratulations to Minyoung, Yuting, and Pritish!
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in … Read the rest
Fangda Han
2019-08-11
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual … Read the rest
This is a joint work by Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic.
For unsupervised domain adaptation, the target domain error can be provably reduced by having a shared input representation that makes the source and target domains … Read the rest