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Found 22 papers, 0 papers with code
Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge
Conventional approaches to facial expression recognition primarily focus on the classification of six basic facial expressions.
1DFormer: a Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking
For temporal modeling, we propose a recurrent token mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a confidence-enhanced multi-head attention mechanism to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group structure modeling mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers.
MEDIC: A Multimodal Empathy Dataset in Counseling
We also propose three labels (i. e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients.
Facial Affective Behavior Analysis Method for 5th ABAW Competition
Facial affective behavior analysis is important for human-computer interaction.
Emotional Reaction Intensity Estimation Based on Multimodal Data
In this paper, 1. better features are extracted with the SOTA pretrained models.
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns.
Ranked #1 on
Video Emotion Recognition
on Ekman6
(using extra training data)
Multi-Task Learning for Emotion Descriptors Estimation at the fourth ABAW Challenge
The 4th competition on affective behavior analysis in the wild (ABAW) provided images with valence/arousal, expression and action unit labels.
Hand-Assisted Expression Recognition Method from Synthetic Images at the Fourth ABAW Challenge
Learning from synthetic images plays an important role in facial expression recognition task due to the difficulties of labeling the real images, and it is challenging because of the gap between the synthetic images and real images.
Facial Expression Recognition
Facial Expression Recognition (FER)
Facial Action Unit Recognition Based on Transfer Learning
Then we fine-tune the network for facial action unit recognition.
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition
To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition.
Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition
Specifically, the proposed deep semi-supervised AU recognition approach consists of a deep recognition network and a discriminator D. The deep recognition network R learns facial representations from large-scale facial images and AU classifiers from limited ground truth AU labels.
Pose-aware Adversarial Domain Adaptation for Personalized Facial Expression Recognition
The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning.
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates.
Multiple Face Analyses through Adversarial Learning
Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks, such as facial landmark localization, head pose estimation, gender recognition and face attribute estimation from image representation-level.
Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey
Affective computing (AC) of these data can help to understand human behaviors and enable wide applications.
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning.
Weakly Supervised Facial Action Unit Recognition Through Adversarial Training
Then we propose a weakly supervised AU recognition method via an adversarial process, in which we simultaneously train two models: a recognition model R, which learns AU classifiers, and a discrimination model D, which estimates the probability that AU labels generated from domain knowledge rather than the recognized AU labels from R. The training procedure for R maximizes the probability of D making a mistake.
A Multimodal Deep Regression Bayesian Network for Affective Video Content Analyses
After that, a joint representation is extracted from the top layers of the two deep networks, and thus captures the high order dependencies between visual modality and audio modality.
Deep Facial Action Unit Recognition From Partially Labeled Data
To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data.
Learning with Privileged Information for Multi-Label Classification
In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information.
Facial Expression Intensity Estimation Using Ordinal Information
In fully supervised case, all the frames are provided with intensity annotations.
Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition
Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited.
Facial Expression Recognition
Facial Expression Recognition (FER)
