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Related Experiment Video

Updated: Jun 3, 2025

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GSE: A global-local storage enhanced video object recognition model.

Yuhong Shi1, Hongguang Pan2, Ze Jiang3

  • 1College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China; Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security, Xi'an, 710054, China; National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi'an, 710054, China; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710054, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Global-Local Storage Enhanced (GSE) model to improve video object recognition by efficiently processing redundant video data. The GSE model enhances feature aggregation and attention mechanisms, achieving superior performance on benchmark datasets.

Keywords:
Cascading multi-head attentionGlobal–local storageMulti-frame aggregationVideo object recognition

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Video object recognition models struggle with performance degradation due to similar and redundant information in video data.
  • Existing methods often fail to efficiently capture and utilize temporal information across video frames.

Purpose of the Study:

  • To propose a novel Global-Local Storage Enhanced (GSE) video object recognition model to overcome limitations of existing approaches.
  • To enhance the efficiency and accuracy of video object recognition by effectively managing redundant information and temporal dependencies.

Main Methods:

  • A two-stage dynamic multi-frame aggregation module is used to reduce computational load while retaining essential frame features.
  • A Global-Local Storage (GS) module filters and retains features using temporal difference thresholds and a storage approach.
  • A Cascaded Multi-head Attention (CMA) mechanism progressively focuses on object features and their correlations, ensuring computational efficiency.

Main Results:

  • The GSE model achieved state-of-the-art mean Average Precision (mAP) scores of 0.8352 on ImageNet 2015 and 0.8617 on NPS-Drones.
  • The model demonstrated a significant reduction in computational burden compared to other methods.
  • The GSE model achieved a strong balance between precision, efficiency, and power consumption.

Conclusions:

  • The proposed GSE model effectively addresses redundancy in video data for improved object recognition.
  • The integration of dynamic multi-frame aggregation, global-local storage, and cascaded attention mechanisms leads to enhanced performance.
  • The GSE model offers a promising solution for efficient and accurate video object recognition in complex scenes.