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

With the transition of facial expression recognition (FER) from laboratory-controlled to
challenging in-the-wild conditions and the recent success of deep learning techniques in …

Human multimodal emotion recognition involves time-series data of different modalities,
such as natural language, visual motions, and acoustic behaviors. Due to the variable …

Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

Affective computing is an emerging area of research that aims to enable intelligent systems
to recognize, feel, infer and interpret human emotions. The widely spread online and off-line …

Many researchers have been seeking robust emotion recognition system for already last two
decades. It would advance computer systems to a new level of interaction, providing much …

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 …

Perceiving human emotions from a multimodal perspective has received significant attention
in knowledge engineering communities. Due to the variable receiving frequency for …

Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning is to create models that can …

Videos flow as the mixture of language, acoustic, and vision modalities. A thorough video
understanding needs to fuse time-series data of different modalities for prediction. Due to the …