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Emotion Recognition
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Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition
논문
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Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study
Divergent Emotional Patterns in Disinformation on Social Media? An Analysis of Tweets and TikToks about the DANA in Valencia
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model
Why disentanglement-based speaker anonymization systems fail at preserving emotions?
Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition
LLM supervised Pre-training for Multimodal Emotion Recognition in Conversations
Uncertainty Estimation in the Real World: A Study on Music Emotion Recognition
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations
AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair Analysis
Synthetic Data Generation by Supervised Neural Gas Network for Physiological Emotion Recognition Data
Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling for Multimodal Emotion Analysis
Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines
XMusic: Towards a Generalized and Controllable Symbolic Music Generation Framework
CG-MER: A Card Game-based Multimodal dataset for Emotion Recognition
EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
Empathetic Conversational Agents: Utilizing Neural and Physiological Signals for Enhanced Empathetic Interactions
Multi-face emotion detection for effective Human-Robot Interaction
A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks
EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model
JELLY: Joint Emotion Recognition and Context Reasoning with LLMs for Conversational Speech Synthesis
MVP: Multimodal Emotion Recognition based on Video and Physiological Signals
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition
Fitting Different Interactive Information: Joint Classification of Emotion and Intention
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition
learning discriminative features from spectrograms using center loss for speech emotion recognition
TED: Turn Emphasis with Dialogue Feature Attention for Emotion Recognition in Conversation
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning
Sample Correlation for Fingerprinting Deep Face Recognition
Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.