Emotional Intelligence

This book constitutes the proceedings of the Second CSIG Conference on Emotional Intelligence, CEI 2024, held in Nanjing, China during December 6-8, 2024.

The 14 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 41 submissions. These papers have been categorized under the following topical sections: Emotional Intelligence Surveys and Databases; Emotional Intelligence Methods; Emotional Intelligence Applications.

Inhalt

.- Emotional Intelligence Surveys and Databases.

.- Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond.

.- REFN: A Multimodal Database for Emotion Analysis Using Functional Near-infrared Spectroscopy.

.- Emotional Intelligence Methods.

.- EIDA: Explicit- and Implicit-space Self-supervised Learning for Visual Emotion Adaptation.

.- A Three streams Convolutional Transformer Fusion Model for Facial Macro- and Micro-Expressions Spotting.

.- Facial Action Unit Recognition with Micro-Action-Aware Transformer.

.- Local and Global Iterative Adaptation Based on Meta learning for Source-free Cross-Corpus Speech Emotion Recognition.

.- Decoupled Representation with Multimodal Prompts for Emotion Recognition in Conversation.

.- Emotional Intelligence Applications.

.- Generative Text Prompts for Image Aesthetic Quality Assessment.

.- Large Language Model Enhanced Fuzzy Logic Fusion Framework for Stance Detection.

.- Skeleton-based Online Action Detection with Temporal Enhancement.

.- Fine-Grained Spatial-Temporal Framework for Engagement Prediction.

.- Multimodal Engagement Recognition by fusing Transformer and Bi-LSTM.

.- Emotional Interaction Hardware Design for Wrist Rehabilitation Based on Secondary Fuzzy Reasoning.

.- Attention-Based Audio Depression Recognition Integrating Handcrafted and Deep Features.

.- STC-ND: Leveraging Spatialtemporal Characteristics with NeXtVLAD for Depression Detection from Few-Channel EEG Signals.

.- DepLLM: Fine-Tuning Large Language Models with a Chinese Dialogue Dataset for Depression Diagnosis via Mixture of Specialized Experts.