Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Blog Article

Attachment systems, which signify emotional bonds with significant others, play a ilootpaperie crucial role in influencing self-development and social interactions.Research on adult attachment in psychology has predominantly utilized questionnaires and interviews, focusing mainly on romantic relationships and parent-child interactions during childhood.Although machine learning approaches in adult attachment research have begun to assess non-verbal behaviors objectively, the connection between these behaviors and attachment styles has not yet been fully explored.

This paper presents Attach-SwiNet, a new multimodal model for classifying attachment styles in close relationships among young adults.Our model combines representations of emotions from non-verbal behaviors miracle academy clothing with subjective responses to categorize attachment styles.It utilizes pre-trained Swin Transformers to analyze emotional cues in facial expression videos and pre-trained ResNet50 to examine speech responses.

By integrating the most effective emotion representations from both datasets with rating data from the Experiences in Close Relationships - Relationship Structures (ECR-RS), our model significantly enhances the accuracy of classifying attachment styles.Experimental results show that our approach improves performance over traditional unimodal behavioral data and subjective questionnaire responses by 1.13%.