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Found 10 papers, 1 papers with code
Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset
Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions.
An Enhanced Adversarial Network with Combined Latent Features for Spatio-Temporal Facial Affect Estimation in the Wild
This paper addresses these shortcomings by proposing a novel model that efficiently extracts both spatial and temporal features of the data by means of its enhanced temporal modelling based on latent features.
Adversarial-based neural networks for affect estimations in the wild
However, the use of latent features, which is feasible through adversarial learning, is not largely explored, yet.
End-to-end facial and physiological model for Affective Computing and applications
In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions.
Reference-based Variational Autoencoders
Given a pool of unlabelled images, the goal is to learn a representation where a set of target factors are disentangled from others.
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.
Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection
Given a set of 3D-to-2D matches, we formulate pose estimation problem as a low-rank homogeneous sys- tem where the solution lies on its 1D null space.
