Emotion analysis, an integral aspect of human–machine interactions, has witnessed
significant advancements in recent years. With the rise of multimodal data sources such as …

While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and
advancement, there are still gaps in defining a more holistic research target seamlessly …

Abstract Multimodal Emotion Recognition in Conversations (MERC) aims to classify
utterance emotions using textual, auditory, and visual modal features. Most existing MERC …

Multimodal affective computing (MAC) has garnered increasing attention due to its broad
applications in analyzing human behaviors and intentions, especially in text-dominated …

Emotion recognition is a crucial task for human conversation understanding. It becomes
more challenging with the notion of multimodal data, eg, language, voice, and facial …

The multimodal emotion-cause pair extraction (MECPE) task aims to detect the emotions,
causes, and emotion-cause pairs from multimodal conversations. Existing methods for this …

Multimodal Federated Learning (MFL) addresses the setup of multiple clients with diversified
modality types (eg image, text, video, and audio) working together to improve their local …

Being able to experience emotions is a defining characteristic of machine intelligence, and
the first step in giving robots emotions is to enable them to accurately recognize and …

Emotion Recognition in Conversations (ERCs) is a vital area within multimodal interaction
research, dedicated to accurately identifying and classifying the emotions expressed by …

Multimodal Emotion Recognition in Conversations (MERC) is an important topic in human-
computer interaction. In the MERC task, conversations exhibit dynamic emotional …