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Multimodal Sentiment Analysis
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Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.
<span style="color:grey; opacity: 0.6">( Image credit: ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition
Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture
Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
DeepMLF: Multimodal language model with learnable tokens for deep fusion in sentiment analysis
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems
MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition
LLaVAC: Fine-tuning LLaVA as a Multimodal Sentiment Classifier
Multi-Modality Collaborative Learning for Sentiment Analysis
Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis
DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
Semi-IIN: Semi-supervised Intra-inter modal Interaction Learning Network for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis based on Video and Audio Inputs
Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content
EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding
Bridging the Gap for Test-Time Multimodal Sentiment Analysis
Multimodal Sentiment Analysis Based on Causal Reasoning
Multimodal Sentiment Analysis Based on BERT and ResNet
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-Attention
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis
Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis
GSIFN: A Graph-Structured and Interlaced-Masked Multimodal Transformer-based Fusion Network for Multimodal Sentiment Analysis
Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
Sentiment Reasoning for Healthcare
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis
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