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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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Emotion Recognition in Contemporary Dance Performances Using Laban Movement Analysis
Spatiotemporal Emotional Synchrony in Dyadic Interactions: The Role of Speech Conditions in Facial and Vocal Affective Alignment
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
Real-Time Imitation of Human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
PhysioSync: Temporal and Cross-Modal Contrastive Learning Inspired by Physiological Synchronization for EEG-Based Emotion Recognition
Visual and textual prompts for enhancing emotion recognition in video
Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis
Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation
CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition
Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models
Attributes-aware Visual Emotion Representation Learning
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
Emotion Recognition Using Convolutional Neural Networks
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Modeling Challenging Patient Interactions: LLMs for Medical Communication Training
Leveraging LLMs with Iterative Loop Structure for Enhanced Social Intelligence in Video Question Answering
OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs
Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations
Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation
Deep Learning for Speech Emotion Recognition: A CNN Approach Utilizing Mel Spectrograms
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
Unifying EEG and Speech for Emotion Recognition: A Two-Step Joint Learning Framework for Handling Missing EEG Data During Inference
Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics
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