29 citations · 181 across the 51 of their papers we have counts for
8 papers · 1 filter
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations
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. However,…
MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs
Fangda Han, Guoyao Hao, Ricardo Guerrero +1
Multilabel conditional image generation is a challenging problem in computer vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a conditional Generative Neural…
Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning
Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic
Computational food analysis (CFA) naturally requires multi-modal evidence of a particular food, e.g., images, recipe text, etc. A key to making CFA possible is multi-modal shared r…
Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach
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. Our assumption is that th…
Recursive Inference for Variational Autoencoders
Minyoung Kim, Vladimir Pavlovic
Inference networks of traditional Variational Autoencoders (VAEs) are typically amortized, resulting in relatively inaccurate posterior approximation compared to instance-wise vari…
Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images
Jiatong Li, Fangda Han, Ricardo Guerrero +1
Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize…