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Facial Action Unit Detection

Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising.

( Image credit: Self-supervised Representation Learning from Videos for Facial Action Unit Detection )

Papers

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Showing 1–25 of 96 papers

TitleStatusHype
FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding SampleCode0
Towards Unified Facial Action Unit Recognition Framework by Large Language Models—0
Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning—0
Norface: Improving Facial Expression Analysis by Identity NormalizationCode1
Representation Learning and Identity Adversarial Training for Facial Behavior UnderstandingCode2
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units RecognitionCode1
AUD-TGN: Advancing Action Unit Detection with Temporal Convolution and GPT-2 in Wild Audiovisual Contexts—0
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection—0
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective ComputingCode1
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit DetectorsCode2
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Learning Contrastive Feature Representations for Facial Action Unit DetectionCode0
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond—0
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction—0
FG-Net: Facial Action Unit Detection with Generalizable Pyramidal FeaturesCode1
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit DetectionCode0
Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection—0
Fighting over-fitting with quantization for learning deep neural networks on noisy labels—0
Multi-modal Facial Action Unit Detection with Large Pre-trained Models for the 5th Competition on Affective Behavior Analysis in-the-wild—0
Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection—0
Self-supervised Facial Action Unit Detection with Region and Relation Learning—0
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning—0
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