814 citations · 1.2k across the 19 of their papers we have counts for
19 papers
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation
Yuting Wang, Ricardo Guerrero, Vladimir Pavlovic
Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes le…
FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training
Adrian Bulat, Ricardo Guerrero, Brais Martinez +1
This paper is on Few-Shot Object Detection (FSOD), where given a few templates (examples) depicting a novel class (not seen during training), the goal is to detect all of its occur…
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition
Victor Escorcia, Ricardo Guerrero, Xiatian Zhu +1
Learning an egocentric action recognition model from video data is challenging due to distractors (e.g., irrelevant objects) in the background. Further integrating object informati…
Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN
Fangda Han, Guoyao Hao, Ricardo Guerrero +1
Multi-attribute conditional image generation is a challenging problem in computervision. We propose Multi-attribute Pizza Generator (MPG), a conditional Generative Neural Network (…
CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval
Hai X. Pham, Ricardo Guerrero, Jiatong Li +1
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 construc…
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…
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…
CookGAN: Meal Image Synthesis from Ingredients
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. Prev…
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…
The Art of Food: Meal Image Synthesis from Ingredients
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 ingredien…
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes
Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero +3
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD…
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
Christopher Bowles, Liang Chen, Ricardo Guerrero +7
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not onl…
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Amir Alansary, Loic Le Folgoc, Ghislain Vaillant +11
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to pe…
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes c…
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)
Yuria Utsumi, Ognjen Rudovic, Kelly Peterson +2
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (A…
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero +1
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) ba…
Spectral Graph Convolutions for Population-based Disease Prediction
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as…