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Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach
Variational autoencoder (VAE) is a very successful generative model whos...
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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...
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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...
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MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs
Multilabel conditional image generation is a challenging problem in comp...
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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...
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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 ...
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Recursive Inference for Variational Autoencoders
Inference networks of traditional Variational Autoencoders (VAEs) are ty...
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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...
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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...
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CookGAN: Meal Image Synthesis from Ingredients
In this work we propose a new computational framework, based on generati...
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Deep Crowd-Flow Prediction in Built Environments
Predicting the behavior of crowds in complex environments is a key requi...
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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...
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Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
Domain Adaptation (DA), the process of effectively adapting task models ...
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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...
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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...
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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...
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Efficient Deep Gaussian Process Models for Variable-Sized Input
Deep Gaussian processes (DGP) have appealing Bayesian properties, can ha...
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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...
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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...
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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...
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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...
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Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation...
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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 ...
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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...
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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...
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Unsupervised Domain Adaptation with Copula Models
We study the task of unsupervised domain adaptation, where no labeled da...
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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...
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Robust Time-Series Retrieval Using Probabilistic Adaptive Segmental Alignment
Traditional pairwise sequence alignment is based on matching individual ...
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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...
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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 ...
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Intrinsic Non-stationary Covariance Function for Climate Modeling
Designing a covariance function that represents the underlying correlati...
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D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM
Bayesian models provide a framework for probabilistic modelling of compl...
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Gaussian Process for Noisy Inputs with Ordering Constraints
We study the Gaussian Process regression model in the context of trainin...
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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...
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Multi-Cue Structure Preserving MRF for Unconstrained Video Segmentation
Video segmentation is a stepping stone to understanding video context. V...
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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...
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Variational Learning in Mixed-State Dynamic Graphical Models
Many real-valued stochastic time-series are locally linear (Gassian), bu...
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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...
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Learning Hypergraph Labeling for Feature Matching
This study poses the feature correspondence problem as a hypergraph node...
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