Search Results for author: Vladimir Pavlovic

Found 46 papers, 11 papers with code

Laying the Foundations of Deep Long-Term Crowd Flow Prediction

1 code implementation ECCV 2020 Samuel S. Sohn, Honglu Zhou, Seonghyeon Moon, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia

Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning.

Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN

1 code implementation22 Oct 2021 Fangda Han, Guoyao Hao, Ricardo Guerrero, Vladimir Pavlovic

To synthesize a pizza image with view attributesoutside the range of natural training images, we design a CGI pizza dataset PizzaView using 3D pizza models and employ it to train a view attribute regressor to regularize the generation process, bridging the real and CGI training datasets.

Conditional Image Generation

DAReN: A Collaborative Approach Towards Reasoning And Disentangling

no code implementations27 Sep 2021 Pritish Sahu, Vladimir Pavlovic

Computational learning approaches to solving visual reasoning tests, such as Raven's Progressive Matrices (RPM), critically depend on the ability of the computational approach to identify the visual concepts used in the test (i. e., the representation) as well as the latent rules based on those concepts (i. e., the reasoning).

Visual Reasoning

Cross-Modal Coherence for Text-to-Image Retrieval

no code implementations22 Sep 2021 Malihe Alikhani, Fangda Han, Hareesh Ravi, Mubbasir Kapadia, Vladimir Pavlovic, Matthew Stone

Common image-text joint understanding techniques presume that images and the associated text can universally be characterized by a single implicit model.

Image Retrieval Text-to-Image Retrieval

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

no code implementations5 Feb 2021 Minyoung Kim, Vladimir Pavlovic

In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of the variational posterior as random Gaussian processes (GP).

Gaussian Processes

CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval

1 code implementation4 Feb 2021 Hai X. Pham, Ricardo Guerrero, Jiatong Li, Vladimir Pavlovic

Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances.

Cross-Modal Retrieval

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

no code implementations23 Dec 2020 Mihee Lee, Vladimir Pavlovic

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities.

Representation Learning

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

1 code implementation4 Dec 2020 Fangda Han, Guoyao Hao, Ricardo Guerrero, Vladimir Pavlovic

Because of the complex nature of the multilabel image generation problem, we also regularize synthetic image by predicting the corresponding ingredients as well as encourage the discriminator to distinguish between matched image and mismatched image.

Conditional Image Generation

Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning

1 code implementation2 Dec 2020 Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic

A key to making CFA possible is multi-modal shared representation learning, which aims to create a joint representation of the multiple views (text and image) of the data.

Cross-Modal Retrieval Image Generation +1

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

no code implementations1 Dec 2020 Minyoung Kim, Ricardo Guerrero, Vladimir Pavlovic

We deal with the problem of learning the underlying disentangled latent factors that are shared between the paired bi-modal data in cross-modal retrieval.

Cross-Modal Retrieval Latent Variable Models

Recursive Inference for Variational Autoencoders

no code implementations NeurIPS 2020 Minyoung Kim, Vladimir Pavlovic

Using the functional gradient approach, we devise an intuitive learning criteria for selecting a new mixture component: the new component has to improve the data likelihood (lower bound) and, at the same time, be as divergent from the current mixture distribution as possible, thus increasing representational diversity.

Variational Inference

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

no code implementations17 Oct 2020 Jiatong Li, Fangda Han, Ricardo Guerrero, Vladimir Pavlovic

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems.

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

no code implementations7 Sep 2020 Minyoung Kim, Vladimir Pavlovic

In deep representational learning, it is often desired to isolate a particular factor (termed {\em content}) from other factors (referred to as {\em style}).

Latent Variable Models

CookGAN: Meal Image Synthesis from Ingredients

1 code implementation25 Feb 2020 Fangda Han, Ricardo Guerrero, Vladimir Pavlovic

In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual list of its ingredients.

Image Generation

Deep Crowd-Flow Prediction in Built Environments

no code implementations13 Oct 2019 Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Mubbasir Kapadia

In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments.

Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation

no code implementations26 Sep 2019 Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic

In this paper, we improve the performance of DA by introducing a discriminative discrepancy measure which takes advantage of auxiliary information available in the source and the target domains to better align the source and target distributions.

Unsupervised Domain Adaptation

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

no code implementations26 Sep 2019 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.

Fast and Effective Adaptation of Facial Action Unit Detection Deep Model

no code implementations26 Sep 2019 Mihee Lee, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic

In this paper, we propose a deep learning approach for facial AU detection that can easily and in a fast manner adapt to a new AU or target subject by leveraging only a few labeled samples from the new task (either an AU or subject).

Action Unit Detection Facial Action Unit Detection +1

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

1 code implementation ICCV 2019 Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic

We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data.

Efficient Deep Gaussian Process Models for Variable-Sized Input

1 code implementation16 May 2019 Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim

The key advantage is that the combination of GP and DRF leads to a tractable model that can both handle a variable-sized input as well as learn deep long-range dependency structures of the data.

Gaussian Processes

The Art of Food: Meal Image Synthesis from Ingredients

1 code implementation9 May 2019 Fangda Han, Ricardo Guerrero, Vladimir Pavlovic

In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual descriptions of its ingredients.

Image Generation

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

no code implementations6 May 2019 Lu Sheng, Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.

Face Model Pose Estimation +1

Relevance Factor VAE: Learning and Identifying Disentangled Factors

1 code implementation5 Feb 2019 Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic

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 meaningful sources of variation and their cardinality.

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

no code implementations21 Mar 2018 Hai X. Pham, Yuting Wang, Vladimir Pavlovic

This paper presents Generative Adversarial Talking Head (GATH), a novel deep generative neural network that enables fully automatic facial expression synthesis of an arbitrary portrait with continuous action unit (AU) coefficients.

Face Model

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

no code implementations2 Oct 2017 Hai X. Pham, Yuting Wang, Vladimir Pavlovic

We present a deep learning framework for real-time speech-driven 3D facial animation from just raw waveforms.

Face Model

Unsupervised Domain Adaptation with Copula Models

no code implementations29 Sep 2017 Cuong D. Tran, Ognjen Rudovic, Vladimir Pavlovic

We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time.

Unsupervised Domain Adaptation

A Generative Model for Depth-Based Robust 3D Facial Pose Tracking

no code implementations CVPR 2017 Lu Sheng, Jianfei Cai, Tat-Jen Cham, Vladimir Pavlovic, King Ngi Ngan

We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.

Face Model Pose Estimation +1

Probabilistic Temporal Subspace Clustering

no code implementations CVPR 2017 Behnam Gholami, Vladimir Pavlovic

In this paper, we propose a unified non-parametric generative framework for temporal subspace clustering to segment data drawn from a sequentially ordered union of subspaces that deals with the missing features in a principled way.

Time Series

Deep Structured Learning for Facial Action Unit Intensity Estimation

no code implementations CVPR 2017 Robert Walecki, Ognjen, Rudovic, Vladimir Pavlovic, Björn Schuller, Maja Pantic

The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning.

Cartoonish sketch-based face editing in videos using identity deformation transfer

no code implementations25 Mar 2017 Long Zhao, Fangda Han, Xi Peng, Xun Zhang, Mubbasir Kapadia, Vladimir Pavlovic, Dimitris N. Metaxas

We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.

Face Model

Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment

no code implementations26 Sep 2016 Shahriar Shariat, Vladimir Pavlovic

Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints.

EEG General Classification +1

Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity

no code implementations CVPR 2016 Robert Walecki, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic

Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6).

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

no code implementations13 Oct 2015 Robert Walecki, Ognjen Rudovic, Vladimir Pavlovic, Maja Pantic

For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive.

Action Unit Detection Facial Expression Recognition

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

no code implementations10 Jul 2015 Hai X. Pham, Chongyu Chen, Luc N. Dao, Vladimir Pavlovic, Jianfei Cai, Tat-Jen Cham

We introduce a novel robust hybrid 3D face tracking framework from RGBD video streams, which is capable of tracking head pose and facial actions without pre-calibration or intervention from a user.

3D Reconstruction Face Model

Intrinsic Non-stationary Covariance Function for Climate Modeling

no code implementations9 Jul 2015 Chintan A. Dalal, Vladimir Pavlovic, Robert E. Kopp

Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models.

Discovering Characteristic Landmarks on Ancient Coins using Convolutional Networks

no code implementations30 Jun 2015 Jongpil Kim, Vladimir Pavlovic

We also propose a new framework to recognize the Roman coins which exploits hierarchical structure of the ancient Roman coins using the state-of-the-art classification power of the CNNs adopted to a new task of coin classification.

General Classification Hierarchical structure

Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

no code implementations30 Jun 2015 Changkyu Song, Sejong Yoon, Vladimir Pavlovic

We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning.

Distributed Optimization Structure from Motion

Gaussian Process for Noisy Inputs with Ordering Constraints

no code implementations30 Jun 2015 Cuong Tran, Vladimir Pavlovic, Robert Kopp

We study the Gaussian Process regression model in the context of training data with noise in both input and output.

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

no code implementations22 Jan 2013 Ognjen Rudovic, Maja Pantic, Vladimir Pavlovic

We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF).

General Classification

Distributed Probabilistic Learning for Camera Networks with Missing Data

no code implementations NeurIPS 2012 Sejong Yoon, Vladimir Pavlovic

In this work we present an approach to estimation and learning of generative probabilistic models in a distributed context where certain sensor data can be missing.

Structure from Motion

Scalable Algorithms for String Kernels with Inexact Matching

no code implementations NeurIPS 2008 Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic

We present a new family of linear time algorithms based on sufficient statistics for string comparison with mismatches under the string kernels framework.

General Classification Genre classification +1

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