Latest Research in Emotion and Mood Recognition
31 research papers · 2026 median publication year
Top Research Topics in Emotion and Mood Recognition
- Emotion and Mood Recognition — 12 papers
- Computer Vision and Pattern Recognition — 5 papers
- Computation and Language — 3 papers
- Artificial Intelligence — 2 papers
- Multimedia — 2 papers
- Sentiment Analysis and Opinion Mining — 2 papers
- Video Analysis and Summarization — 1 papers
- Machine Learning — 1 papers
- Sound — 1 papers
- Human-Computer Interaction — 1 papers
Highest-Cited Papers
- Adaptive learning behavior recognition using multimodal transformer based dynamic modality regulation
- Dual-stream audio–visual Transformer fusion for harmful video content detection
- Reading Emotions in the Token Space: Discriminative Adaptation of SpeechLLMs for Emotion Recognition
- Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition
- Evaluating early, late, hybrid and meta fusion in multimodal emotion detection with pretrained models
- Affect-Prototype Guided Fusion for Open-Vocabulary Incomplete Multi-modal Emotion Recognition
- Multimodal transformer and directed relational graph learning for emotion recognition in conversations with potential applications in college student mental health research
- QESM: A Quantum-Inspired Network with Dynamic Fusion and Entangled Measurement for multimodal emotion recognition
- ReH-FUSE: Reliability-Aware Hierarchical Fusion of Experts for Multimodal Emotion Recognition in Conversation
- Multimodal Temporal Modeling for Continuous Group Emotion Recognition in Multi-party Dialogues
- Multimodal Emotion Recognition in Conversations via Class-Wise Adaptive Modality Fusion and Affective Geometry
- RAFM-SER++: A Lightweight Multimodal Emotion Recognition Framework for Real-Time Behavioral Monitoring in Surveillance Systems
- Enhancing Multimodal Emotion Recognition via Multi-Feature Encoding and Attention-Based Fusion
- Emotion as a Distribution: Joint Valence-Arousal Probability Learning for Speaker-Independent Multimodal Emotion Recognition
- KTU-MEDAFE: A Newly Developed Multimodal Dataset for Emotion Recognition Using EEG–Speech Decision-Level Fusion
- MAFT: a multimodal attention fusion transformer framework with frozen pretrained encoders for conversational emotion recognition
- Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS
- TEIDAN: A Multilingual Multiparty Dialogue Corpus
- Mathematical modelling of attention-guided deep learning for real-time text emotion recognition in assistive interaction systems for disabled persons
- Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective