Search Results for author:
Found 14 papers, 3 papers with code
Deep adaptative spectral zoom for improved remote heart rate estimation
The objective of our proposed model is to tailor the CZT to match the characteristics of each specific dataset sensor, facilitating a more optimal and accurate estimation of HR from the rPPG signal without compromising generalization across diverse datasets.
Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fields
To quantify the uncertainty on the learned surface, we model a stochastic radiance field.
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
For this purpose, we build a residual-like permutation-invariant network that successfully refines potentially corrupted initial 3D poses estimated by an off-the-shelf detector.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+1
Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss
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 of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets between the facial video PPG and the ground truth.
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
We address the problem of multi-person 3D body pose and shape estimation from a single image.
Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations
In this context, we propose Stochastic Neural Radiance Fields (S-NeRF), a generalization of standard NeRF that learns a probability distribution over all the possible radiance fields modeling the scene.
Generating Attribution Maps with Disentangled Masked Backpropagation
Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks.
Anytime Inference with Distilled Hierarchical Neural Ensembles
We propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks in a hierarchical tree structure, sharing intermediate layers.
Adaptative Inference Cost With Convolutional Neural Mixture Models
Despite the outstanding performance of convolutional neural networks (CNNs) for many vision tasks, the required computational cost during inference is problematic when resources are limited.
Learning Disentangled Representations with Reference-Based Variational Autoencoders
Given a pool of unlabeled images, the goal is to learn a representation where a set of target factors are disentangled from others.
Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised Facial Behavior Analysis
In this framework, we treat instance-labels as temporally-dependent latent variables in an Undirected Graphical Model.
Multi-instance Dynamic Ordinal Random Fields for Weakly-Supervised Pain Intensity Estimation
In this paper, we address the Multi-Instance-Learning (MIL) problem when bag labels are naturally represented as ordinal variables (Multi--Instance--Ordinal Regression).
From Emotions to Action Units With Hidden and Semi-Hidden-Task Learning
Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.
