24 citations · 64 across the 10 of their papers we have counts for
11 papers
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo +2
The recovery of multi-person 3D poses from a single RGB image is a severely ill-conditioned problem due to the inherent 2D-3D depth ambiguity, inter-person occlusions, and body tru…
Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss
Joaquim Comas, Adria Ruiz, Federico Sukno
We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling…
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
Jianxiong Shen, Antonio Agudo, Francesc Moreno-Noguer +1
A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geome…
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
Nicolas Ugrinovic, Adria Ruiz, Antonio Agudo +2
We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple time…
Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations
Jianxiong Shen, Adria Ruiz, Antonio Agudo +1
Neural Radiance Fields (NeRF) has become a popular framework for learning implicit 3D representations and addressing different tasks such as novel-view synthesis or depth-map estim…
Generating Attribution Maps with Disentangled Masked Backpropagation
Adria Ruiz, Antonio Agudo, Francesc Moreno
Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks. In this task, the…
Anytime Inference with Distilled Hierarchical Neural Ensembles
Adria Ruiz, Jakob Verbeek
Inference in deep neural networks can be computationally expensive, and networks capable of anytime inference are important in mscenarios where the amount of compute or quantity of…
Adaptative Inference Cost With Convolutional Neural Mixture Models
Adria Ruiz, Jakob Verbeek
Despite the outstanding performance of convolutional neural networks (CNNs) for many vision tasks, the required computational cost during inference is problematic when resources ar…
Learning Disentangled Representations with Reference-Based Variational Autoencoders
Adria Ruiz, Oriol Martinez, Xavier Binefa +1
Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. So…
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
Adria Ruiz, Ognjen Rudovic, Xavier Binefa +1
We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised learning problems, where a training set is formed by bags (sets of feature vectors or instances) and only…
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
Adria Ruiz, Ognjen Rudovic, Xavier Binefa +1
In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression). Moreover,…