I’m very glad to have our work CookGAN recognized by the CV/ML community SyncedReview on Medium! We are working on building a demo webpage for it.
CookGAN recognized by the CV/ML community
CookGAN: Meal Image Synthesis from Ingredients
Following the previous work that generates meal image from ingredients, we extends it with more experiments in our new paper that has been accepted by WACV 2020.
Paper
Arxiv: https://arxiv.org/abs/2002.11493
- F. Han, R. Guerrero, and V. Pavlovic, “CookGAN: Meal Image
Samsung Best Paper Award 2019
Proud to have received a Samsung Best Paper Award for 2019 with my team at SAIC Cambridge.
Although only Minyoung and I are listed as the recipients, this work would not have been possible without my students Pritish Sahu … Read the rest
Keynote at 1st Chengdu International Forum on Frontier Medicine
Honored to give a keynote on AI & Nutrition at the 1st Chengdu Int’l 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