The omnipresence of numerous information sources in our daily life brings up new
alternatives for emotion recognition in several domains including e-health, e-learning …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

Federated learning (FL) has attracted growing attentions via data-private collaborative
training on decentralized clients. However, most existing methods unrealistically assume …

Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

Capturing emotions within a conversation plays an essential role in modern dialogue
systems. However, the weak correlation between emotions and semantics brings many …

Dialogue emotion detection is always challenging due to human subjectivity and the
randomness of dialogue content. In a conversation, the emotion of each person often …

Capturing complex contextual dependencies plays a vital role in Emotion Recognition in
Conversations (ERC). Previous studies have predominantly focused on speaker-aware …

A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish
semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) …

Emotion recognition in conversation (ERC) has promising potential in many fields, such as
recommendation systems, man–machine interaction, and medical care. In contrast to other …

Multivariate time-series forecasting is a critical task for many applications, and graph time-
series network is widely studied due to its capability to capture the spatial-temporal …