Multimodal emotion recognition (MER) refers to the identification and understanding of
human emotional states by combining different signals, including—but not limited to—text …

Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Human multimodal emotion recognition (MER) aims to perceive human emotions via
language, visual and acoustic modalities. Despite the impressive performance of previous …

Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …

With the proliferation of user-generated online videos, Multimodal Sentiment Analysis (MSA)
has attracted increasing attention recently. Despite significant progress, there are still two …

Human multimodal emotion recognition (MER) aims to perceive and understand human
emotions via various heterogeneous modalities, such as language, vision, and acoustic …

Depression is now a prevalent mental illness and multimodal data-based depression
detection is an essential topic of research. Internet of Medical Things devices can provide …

Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information
from multiple sources (eg, language, video, and audio), the potential sentiment-irrelevant …

Understanding human behaviors and intents from videos is a challenging task. Video flows
usually involve time-series data from different modalities, such as natural language, facial …