814 citations · 1.2k across the 19 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…