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Yuchen Wang1, Linbo Qing1, Zhengyong Wang1

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

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Summary
This summary is machine-generated.

This study introduces a novel Multi-level Transformer-Based Social Relation Recognition (MT-SRR) framework. MT-SRR effectively orchestrates multi-scale features for improved social relation recognition and addresses data imbalance challenges.

Keywords:
data drivensocial intelligencesocial relation recognitiontransformer

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Social relationships are crucial for understanding human behavior and developing intelligent systems.
  • Existing social relation recognition (SRR) methods struggle to comprehensively integrate features of varying importance.
  • A need exists for advanced SRR frameworks that can effectively fuse multi-scale information.

Purpose of the Study:

  • To propose a novel Multi-level Transformer-Based Social Relation Recognition (MT-SRR) framework.
  • To enhance the orchestration of multi-scale features for more accurate SRR.
  • To address the challenge of data imbalance in social relation recognition tasks.

Main Methods:

  • Utilized a Vision Transformer (ViT) for global feature extraction.
  • Introduced an Intra-Relation Transformer (Intra-TRM) for dynamic feature fusion.
  • Employed an Inter-Relation Transformer (Inter-TRM) to leverage logical constraints among relationships.
  • Incorporated a margin-based loss function to mitigate data imbalance.

Main Results:

  • The MT-SRR framework demonstrated superior fusion of multi-scale features.
  • The model effectively ameliorated the negative impact of data imbalance.
  • Experimental results showed significant improvements over state-of-the-art methods on benchmark datasets.

Conclusions:

  • MT-SRR offers a robust approach for social relation recognition by effectively integrating multi-level features.
  • The proposed framework advances the development of more sophisticated social intelligent systems.
  • MT-SRR provides a significant performance improvement in recognizing social relationships, even with imbalanced data.