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
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
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
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
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
Traditional computer vision algorithms, particularly those that exploit various probabilistic and learning-based approaches, are often formulated in centralized settings. However, modern computational settings are becoming increasingly characterized by networks of peer-to-peer connected devices, with local data processing abilities. … Read the rest
Recognition of Ancient Roman Coins
1. Problem Formulation
For a given Roman coin image, the goal is to recognize who is on the coin
There are thousand of different ways to define the Roman coins. For example, we can … Read the rest