Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

The POSTER has achieved SOTA performance in facial expression recognition (FER) by
effectively combining facial landmarks and image features through its two-stream pyramid …

Abstract Noisy label Facial Expression Recognition (FER) is more challenging than
traditional noisy label classification tasks due to the inter-class similarity and the annotation …

Facial expression recognition (FER) has received increasing interest in computer vision. We
propose the TransFER model which can learn rich relation-aware local representations. It …

Facial Expression Recognition (FER) in the wild is an extremely challenging task. Recently,
some Vision Transformers (ViT) have been explored for FER, but most of them perform …

Facial expression recognition (FER) is an emerging and multifaceted research topic.
Applications of FER in healthcare, security, safe driving, and so forth have contributed to the …

Over the years, a number of semisupervised deep‐learning algorithms have been proposed
for time‐series classification (TSC). In semisupervised deep learning, from the point of view …

Current benchmarks for facial expression recognition (FER) mainly focus on static images,
while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether …

Only parts of unlabeled data are selected to train models for most semi-supervised learning
methods, whose confidence scores are usually higher than the pre-defined threshold (ie, the …

Facial expression recognition (FER) is an important task in computer vision, having practical
applications in areas such as human-computer interaction, education, healthcare, and …