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Using Multi-Layer Bidirectional Distillation to Enhance Local and Global Features for Action Recognition.

Shilu Kang1, Hua Huo1, Jiaxin Xu1

  • 1College of Information Engineering and Artificial Intelligence, Henan University of Science and Technology, Luoyang 471000, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
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This study introduces a Multi-Layer Bidirectional Distillation Model (MBD) for action recognition. The MBD model effectively balances local and global features, improving accuracy in both short and long videos.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Action recognition accuracy depends on balancing local and global video features.
  • Long-video understanding presents challenges in dynamically integrating these features.

Purpose of the Study:

  • To propose a Multi-Layer Bidirectional Distillation Model (MBD) for enhanced action recognition.
  • To explore synergistic enhancement between local and global features using a two-stream architecture.

Main Methods:

  • Utilized 3D CNN for local and Video Transformer for global spatio-temporal features.
  • Implemented bidirectional knowledge distillation at intermediate and final layers.
  • Employed an adaptive fusion strategy based on feature dominance for dynamic weighted summation.
Keywords:
3D CNNcomplementary mechanismsfeature fusionhuman action recognitionknowledge distillationvideo Transformer

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Main Results:

  • The MBD model demonstrated superior performance across four classic action recognition benchmarks.
  • Achieved improved accuracy in both short-video recognition and complex long-video scenarios.
  • Effectively suppressed noise from non-dominant features while maximizing dominant feature advantages.

Conclusions:

  • The MBD model offers a novel approach to action recognition by effectively integrating local and global features.
  • Bidirectional distillation and adaptive fusion strategies overcome limitations of traditional methods.
  • The model shows significant potential for real-world video understanding applications.