Vladimir Pavlovic

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Rutgers University ∙ SAMSUNG

Professor at Rutgers University

Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach

Variational autoencoder (VAE) is a very successful generative model whos...
02/05/2021 ∙ by Minyoung Kim, et al. ∙ 0 ∙ share

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CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval

Despite the abundance of multi-modal data, such as image-text pairs, the...
02/04/2021 ∙ by Hai X. Pham, et al. ∙ 7 ∙ share

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Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations

Multi-modal generative models represent an important family of deep mode...
12/23/2020 ∙ by Mihee Lee, et al. ∙ 9 ∙ share

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MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs

Multilabel conditional image generation is a challenging problem in comp...
12/04/2020 ∙ by Fangda Han, et al. ∙ 11 ∙ share

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Cross-modal Retrieval and Synthesis (X-MRS): Closing the modality gap in shared subspace

Computational food analysis (CFA), a broad set of methods that attempt t...
12/02/2020 ∙ by Ricardo Guerrero, et al. ∙ 0 ∙ share

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Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach

We deal with the problem of learning the underlying disentangled latent ...
12/01/2020 ∙ by Minyoung Kim, et al. ∙ 0 ∙ share

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Recursive Inference for Variational Autoencoders

Inference networks of traditional Variational Autoencoders (VAEs) are ty...
11/17/2020 ∙ by Minyoung Kim, et al. ∙ 0 ∙ share

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Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

Increased awareness of the impact of food consumption on health and life...
10/17/2020 ∙ by Jiatong Li, et al. ∙ 1 ∙ share

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Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models

In deep representational learning, it is often desired to isolate a part...
09/07/2020 ∙ by Minyoung Kim, et al. ∙ 3 ∙ share

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CookGAN: Meal Image Synthesis from Ingredients

In this work we propose a new computational framework, based on generati...
02/25/2020 ∙ by Fangda Han, et al. ∙ 35 ∙ share

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Deep Crowd-Flow Prediction in Built Environments

Predicting the behavior of crowds in complex environments is a key requi...
10/13/2019 ∙ by Samuel S. Sohn, et al. ∙ 14 ∙ share

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Scenario Generalization of Data-driven Imitation Models in Crowd Simulation

Crowd simulation, the study of the movement of multiple agents in comple...
10/02/2019 ∙ by Gang Qiao, et al. ∙ 1 ∙ share

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Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation

Domain Adaptation (DA), the process of effectively adapting task models ...
09/26/2019 ∙ by Behnam Gholami, et al. ∙ 11 ∙ share

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Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

In this paper, we study the novel problem of not only predicting ingredi...
09/26/2019 ∙ by Jiatong Li, et al. ∙ 34 ∙ share

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Fast and Effective Adaptation of Facial Action Unit Detection Deep Model

Detecting facial action units (AU) is one of the fundamental steps in au...
09/26/2019 ∙ by Mihee Lee, et al. ∙ 4 ∙ share

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Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement

We propose a family of novel hierarchical Bayesian deep auto-encoder mod...
09/06/2019 ∙ by Minyoung Kim, et al. ∙ 17 ∙ share

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Efficient Deep Gaussian Process Models for Variable-Sized Input

Deep Gaussian processes (DGP) have appealing Bayesian properties, can ha...
05/16/2019 ∙ by Issam H. Laradji, et al. ∙ 5 ∙ share

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The Art of Food: Meal Image Synthesis from Ingredients

In this work we propose a new computational framework, based on generati...
05/09/2019 ∙ by Fangda Han, et al. ∙ 1 ∙ share

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Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-base...
05/06/2019 ∙ by Lu Sheng, et al. ∙ 7 ∙ share

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Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach

In unsupervised domain adaptation, it is widely known that the target do...
02/23/2019 ∙ by Minyoung Kim, et al. ∙ 5 ∙ share

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Relevance Factor VAE: Learning and Identifying Disentangled Factors

We propose a novel VAE-based deep auto-encoder model that can learn dise...
02/05/2019 ∙ by Minyoung Kim, et al. ∙ 11 ∙ share

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Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation...
10/26/2018 ∙ by Behnam Gholami, et al. ∙ 1 ∙ share

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Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network

This paper presents Generative Adversarial Talking Head (GATH), a novel ...
03/21/2018 ∙ by Hai X. Pham, et al. ∙ 1 ∙ share

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The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation

Trajectory interpolation, the process of filling-in the gaps and removin...
10/10/2017 ∙ by Gang Qiao, et al. ∙ 1 ∙ share

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End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech

We present a deep learning framework for real-time speech-driven 3D faci...
10/02/2017 ∙ by Hai X. Pham, et al. ∙ 1 ∙ share

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Unsupervised Domain Adaptation with Copula Models

We study the task of unsupervised domain adaptation, where no labeled da...
09/29/2017 ∙ by Cuong D. Tran, et al. ∙ 1 ∙ share

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Sketch-based Face Editing in Video Using Identity Deformation Transfer

We address the problem of using hand-drawn sketch to edit facial identit...
03/25/2017 ∙ by Long Zhao, et al. ∙ 1 ∙ share

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Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment

Traditional pairwise sequence alignment is based on matching individual ...
09/26/2016 ∙ by Shahriar Shariat, et al. ∙ 1 ∙ share

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Variable-state Latent Conditional Random Fields for Facial Expression Recognition and Action Unit Detection

Automated recognition of facial expressions of emotions, and detection o...
10/13/2015 ∙ by Robert Walecki, et al. ∙ 1 ∙ share

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Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes

We introduce a novel robust hybrid 3D face tracking framework from RGBD ...
07/10/2015 ∙ by Hai X. Pham, et al. ∙ 1 ∙ share

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Intrinsic Non-stationary Covariance Function for Climate Modeling

Designing a covariance function that represents the underlying correlati...
07/09/2015 ∙ by Chintan A. Dalal, et al. ∙ 1 ∙ share

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D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM

Bayesian models provide a framework for probabilistic modelling of compl...
07/03/2015 ∙ by Behnam Babagholami-Mohamadabadi, et al. ∙ 1 ∙ share

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Gaussian Process for Noisy Inputs with Ordering Constraints

We study the Gaussian Process regression model in the context of trainin...
06/30/2015 ∙ by Cuong Tran, et al. ∙ 1 ∙ share

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Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks

In this paper, we propose a novel method to find characteristic landmark...
06/30/2015 ∙ by Jongpil Kim, et al. ∙ 1 ∙ share

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Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation

Video segmentation is a stepping stone to understanding video context. V...
06/30/2015 ∙ by Saehoon Yi, et al. ∙ 1 ∙ share

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Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

We propose new methods to speed up convergence of the Alternating Direct...
06/30/2015 ∙ by Changkyu Song, et al. ∙ 1 ∙ share

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Variational Learning in Mixed-State Dynamic Graphical Models

Many real-valued stochastic time-series are locally linear (Gassian), bu...
01/23/2013 ∙ by Vladimir Pavlovic, et al. ∙ 1 ∙ share

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Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity Estimation from Facial Images

We propose a novel method for automatic pain intensity estimation from f...
01/22/2013 ∙ by Ognjen Rudovic, et al. ∙ 1 ∙ share

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Learning Hypergraph Labeling for Feature Matching

This study poses the feature correspondence problem as a hypergraph node...
07/13/2011 ∙ by Toufiq Parag, et al. ∙ 1 ∙ share

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Vladimir Pavlovic

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