Główne pojęcia
Effective representation learning for multimodal sentiment analysis through shared and private information capture.
Streszczenie
The content discusses a novel approach to multimodal sentiment analysis by introducing a deep modal shared information learning module. It addresses the challenges of capturing shared and private information across modalities, proposing a self-supervised multi-task learning strategy. The method aims to enhance performance by focusing on modal differentiation during training. Extensive experiments validate the model's effectiveness in capturing nuanced information in sentiment analysis tasks.
Structure:
Introduction to Multimodal Sentiment Analysis
Leveraging diverse modalities for sentiment analysis.
Recognizing synergies between different modalities.
Challenges in Multimodal Sentiment Analysis
Addressing alignment, translation, representation, fusion, and co-learning challenges.
Emphasizing the importance of capturing shared and private information between modalities.
Proposed Approach: Deep Modal Shared Information Learning Module
Utilizing covariance matrix to capture shared information.
Introducing label generation module for private information.
Experimental Validation
Conducting experiments on benchmark datasets.
Demonstrating superior performance compared to existing methods.
Statystyki
"Our work makes several innovative contributions."
"Experimental results validate the reliability of our model."
"The proposed function utilizes the covariance matrix as a second-order statistic."
Cytaty
"Enhancing the accuracy of MSA hinges on a comprehensive understanding of the shared and private information present in the modalities."
"Our approach demonstrates promise in effectively capturing this information."