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CocoAdapter: Efficient end-to-end temporal action detection via self-constrained multi-cognitive adapters.

Lizao Zhang1, Qiuhong Tian1, Junxiao Ning2

  • 1Department of Computer Science and Technology,Zhejiang Sci-Tech University, No. 928, 2nd Street, Qianjiang District, Hangzhou, 310018, Zhejiang, China.

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Summary
This summary is machine-generated.

This study introduces CocoAdapter to reduce memory usage in temporal action detection (TAD) training. The novel CocoTad model achieves state-of-the-art performance by efficiently fine-tuning adapter parameters.

Keywords:
Adapter tuningEnd-to-end trainingParameter-efficient tuningTemporal action detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • End-to-end training in Temporal Action Detection (TAD) enhances performance by optimizing video encoders and action classifiers.
  • Significant memory bottlenecks hinder the effectiveness of current end-to-end TAD training methods.

Purpose of the Study:

  • To address memory constraints in end-to-end TAD by introducing a novel adapter module.
  • To develop a specialized adapter, CocoAdapter, designed for TAD tasks.
  • To establish a new baseline model, CocoTad, utilizing the proposed adapter.

Main Methods:

  • Proposed CocoAdapter with self-constraint projection layers to adapt convolutional groups based on network depth.
  • Implemented a fine-tuning strategy where only adapter parameters are updated, reducing computational overhead.
  • Constructed the CocoTad baseline integrating the CocoAdapter for end-to-end TAD.

Main Results:

  • CocoTad significantly reduces memory consumption during end-to-end training.
  • The proposed method achieves state-of-the-art performance on four benchmark datasets.
  • Evaluations demonstrate superior mean Average Precision (mAP) compared to existing methods.

Conclusions:

  • CocoAdapter effectively alleviates memory bottlenecks in end-to-end TAD.
  • CocoTad offers an efficient and high-performing solution for temporal action detection.
  • The adapter-based approach facilitates practical deployment of advanced TAD models.