29 citations · 181 across the 51 of their papers we have counts for
11 papers · 1 filter
Deep Crowd-Flow Prediction in Built Environments
Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou +3
Predicting the behavior of crowds in complex environments is a key requirement in a multitude of application areas, including crowd and disaster management, architectural design, a…
Scenario Generalization of Data-driven Imitation Models in Crowd Simulation
Gang Qiao, Honglu Zhou, Mubbasir Kapadia +2
Crowd simulation, the study of the movement of multiple agents in complex environments, presents a unique application domain for machine learning. One challenge in crowd simulation…
Deep Cooking: Predicting Relative Food Ingredient Amounts from Images
Jiatong Li, Ricardo Guerrero, Vladimir Pavlovic
In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two…
Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
Behnam Gholami, Pritish Sahu, Minyoung Kim +1
Domain Adaptation (DA), the process of effectively adapting task models learned on one domain, the source, to other related but distinct domains, the targets, with no or minimal re…
Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement
Minyoung Kim, Yuting Wang, Pritish Sahu +1
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data. While many recent attempts at factor…
Fast and Effective Adaptation of Facial Action Unit Detection Deep Model
Mihee Lee, Ognjen Rudovic, Vladimir Pavlovic +1
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…