Abstract Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that
aims to perceive the emotional states of the target person with contextual information …

Context-aware emotion recognition (CAER) has recently boosted the practical applications
of affective computing techniques in unconstrained environments. Mainstream CAER …

Emotion recognition, which is a part of affective computing, draws a lot of attention from
researchers because of its broad applications. Unlike previous approaches with the aim to …

Understanding emotions from diverse contexts has received widespread attention in
computer vision communities. The core philosophy of Context-Aware Emotion Recognition …

Image denoising is a fundamental problem in computer vision and multimedia computation.
Non-local filters are effective for image denoising. But existing deep learning methods that …

For a better intention inference, we often try to figure out the emotional states of other people
in social communications. Many studies on affective computing have been carried out to …

Emotion recognition aims to discern the emotional state of subjects within an image, relying
on subject-centric and contextual visual cues. Current approaches typically follow a two …

Machine vision has had a substantial impact on human‐computer interaction and
psychological research. The ability of robots to generate realistic facial expressions has …

Automatic emotion recognition has recently gained significant attention due to the growing
popularity of deep learning algorithms. One of the primary challenges in emotion recognition …

The recognition of emotions heavily relies on important factors such as human facial
expressions and physiological signals, including electroencephalogram and …