Honored to give a keynote on AI & Nutrition at the 1st Chengdu Int’l Forum on Frontier Medicine.
Keynote at 1st Chengdu International Forum on Frontier Medicine
Deep Cooking: Predicting Relative Food Ingredient Amounts from Images
This is a joint work by Jiatong Li, Ricardo Guerrero and Vladimir Pavlovic. The paper is accepted by the 5th International Workshop on Multimedia Assisted Dietary Management (MADiMa), ACM International Conference on Multimedia (ACMMM2019).
Abstract
In this paper, we study … Read the rest
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
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.
Abstract
… Read the restFast Adaptation of Deep Models for Facial Action Unit Detection Using Model-Agnostic Meta-Learning
Mihee Lee, Ognjen (Oggi) Rudovic, Vladimir Pavlovic, and Maja Pantic
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
Relevance Factor VAE: Learning and Identifying Disentangled Factors
Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic.
Abstract
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
Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network
Hai X. Pham, Yuting Wang & Vladimir Pavlovic.
Abstract
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
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement – ICCV’19 Oral
Our new paper on Bayesian representation learning was accepted as Oral at ICCV 2019. Congratulations to Minyoung, Yuting, and Pritish!
Abstract
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in … Read the rest
The Art of Food: Meal Image Synthesis from Ingredients
Fangda Han
2019-08-11
Abstract
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
cs535 special permission or prerequisite override requests for Fall 2018
If you are interested in registering for my Fall 2018 cs535 Pattern Recognition and do not have the prerequisites or for some other reason need a Special Permission (SPN) or Prerequisite Override, you will need to fill out a request … Read the rest
Rutgers CS ranked #9 in the US in Computer Vision / Machine Learning / IR
We recently received some exceptionally encouraging news: Rutgers CS is ranked #9 in the US in the combined categories of Computer Vision / Machine Learning and Data Mining / Information Retrieval.
This is quite an accomplishment. Congratulations to all!… Read the rest