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Multimodal Emotion Recognition
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This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are A: Acoustic T: Text V: Visual
Please include the modality in the bracket after the model name.
All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition
TACFN: Transformer-based Adaptive Cross-modal Fusion Network for Multimodal Emotion Recognition
PsyCounAssist: A Full-Cycle AI-Powered Psychological Counseling Assistant System
Leveraging Label Potential for Enhanced Multimodal Emotion Recognition
Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusion
BeMERC: Behavior-Aware MLLM-based Framework for Multimodal Emotion Recognition in Conversation
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations
Feature-Based Dual Visual Feature Extraction Model for Compound Multimodal Emotion Recognition
United we stand, Divided we fall: Handling Weak Complementary Relationships for Audio-Visual Emotion Recognition in Valence-Arousal Space
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts
R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning
Latent Distribution Decoupling: A Probabilistic Framework for Uncertainty-Aware Multimodal Emotion Recognition
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion
Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network
LLM supervised Pre-training for Multimodal Emotion Recognition in Conversations
MVP: Multimodal Emotion Recognition based on Video and Physiological Signals
Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment
A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language
EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation
SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognition
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation
Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors
Hierarchical Hypercomplex Network for Multimodal Emotion Recognition
PHemoNet: A Multimodal Network for Physiological Signals
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples
Video Emotion Open-vocabulary Recognition Based on Multimodal Large Language Model
SZTU-CMU at MER2024: Improving Emotion-LLaMA with Conv-Attention for Multimodal Emotion Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.