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Found 18 papers, 1 papers with code
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets.
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events
We hypothesized that available intraoperative mechanical ventilation and physiological time-series data combined with other clinical events could be used to accurately predict missing start and end times of OLV.
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning
Diverse visual stimuli can evoke various human affective states, which are usually manifested in an individual's muscular actions and facial expressions.
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity
By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network.
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision
This is followed by a co-rectification technique designed to mitigate the adverse effects of noisy pseudo-labels.
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection.
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels
Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations.
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes.
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis
In this paper, we propose a compact model to enhance the representational and focusing power of neural attention maps and learn the "inter-attention" correlation for refined attention maps, which we term the "Self-Diversified Multi-Channel Attention Network (SMA-Net)".
Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers
In this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT) method for AU detection, which learns AU encoding features representation from different modalities by transformer encoder and fuses modalities by another fusion transformer module.
The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation
Telehealth has the potential to offset the high demand for help during public health emergencies, such as the COVID-19 pandemic.
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection
The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network.
How Do the Hearts of Deep Fakes Beat? Deep Fake Source Detection via Interpreting Residuals with Biological Signals
Our results indicate that our approach can detect fake videos with 97. 29% accuracy, and the source model with 93. 39% accuracy.
Facial Expression Recognition by De-Expression Residue Learning
We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers.
Facial Expression Recognition
Facial Expression Recognition (FER)
FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge
The FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to the estimation of Action Units occurrence and intensity under different camera views.
EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection
For the enhancing layers, we designed an attention map based on facial landmark features and applied it to a pretrained neural network to conduct enhanced learning (The E-Net).
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions
Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR).
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.
