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Found 7 papers, 0 papers with code
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life.
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty.
Deep Structured Learning for Facial Action Unit Intensity Estimation
The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning.
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation.
Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity
Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6).
Variable-state Latent Conditional Random Fields for Facial Expression Recognition and Action Unit Detection
For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive.
