no code implementations • 2 Dec 2022 • Yuting Wang, Ricardo Guerrero, Vladimir Pavlovic
In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features.
no code implementations • ICCV 2023 • Adrian Bulat, Ricardo Guerrero, Brais Martinez, Georgios Tzimiropoulos
Importantly, we show that our system is not only more flexible than existing methods, but also, it makes a step towards satisfying desideratum (c).
no code implementations • 10 Apr 2022 • Victor Escorcia, Ricardo Guerrero, Xiatian Zhu, Brais Martinez
To overcome both limitations, we introduce Self-Supervised Learning Over Sets (SOS), an approach to pre-train a generic Objects In Contact (OIC) representation model from video object regions detected by an off-the-shelf hand-object contact detector.
1 code implementation • 22 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.
1 code implementation • 4 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.
1 code implementation • 4 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.
1 code implementation • 2 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.
Ranked #5 on Cross-Modal Retrieval on Recipe1M
no code implementations • 1 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.
no code implementations • 17 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.
1 code implementation • 25 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.
no code implementations • 26 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.
1 code implementation • 9 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.
no code implementations • 19 Apr 2019 • Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard
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) in the cognitive domain -- over the future 6, 12, 18, and 24 months.
no code implementations • 25 Oct 2018 • Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, Daniel Rueckert
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets.
no code implementations • 8 Jun 2018 • Amir Alansary, Loic Le Folgoc, Ghislain Vaillant, Ozan Oktay, Yuanwei Li, Wenjia Bai, Jonathan Passerat-Palmbach, Ricardo Guerrero, Konstantinos Kamnitsas, Benjamin Hou, Steven McDonagh, Ben Glocker, Bernhard Kainz, Daniel Rueckert
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
1 code implementation • 5 Jun 2018 • Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, Daniel Rueckert
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
1 code implementation • 22 Feb 2018 • Yuria Utsumi, Ognjen Rudovic, Kelly Peterson, Ricardo Guerrero, Rosalind W. Picard
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 (AD) in the cognitive domain -- using data from each patient's previous visits, and testing on future (held-out) data.
1 code implementation • 1 Dec 2017 • Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero, Rosalind W. Picard
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) based on each patient's previous visits.