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Related Experiment Video

Updated: Feb 8, 2026

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice
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Adaptive detrending to accelerate convolutional gated recurrent unit training for contextual video recognition.

Minju Jung1, Haanvid Lee2, Jun Tani3

  • 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea; Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|June 25, 2018
PubMed
Summary

Adaptive detrending (AD) accelerates training for convolutional recurrent neural networks (ConvRNNs) in long-term video recognition. This method improves generalization and performance, especially for complex contextual tasks.

Keywords:
Convolutional neural networks (CNNs)Convolutional recurrent neural networks (ConvRNNs)DetrendingInternal covariate shiftNormalizationRecurrent neural networks (RNNs)

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video image recognition research has advanced rapidly, but often focuses on short-term analysis.
  • Existing methods for long-term, contextual video recognition, such as Convolutional Recurrent Neural Networks (ConvRNNs), face computational challenges that hinder training speed.

Purpose of the Study:

  • To introduce Adaptive Detrending (AD) as a novel temporal normalization technique.
  • To accelerate the training of ConvRNNs, particularly Convolutional Gated Recurrent Units (ConvGRUs), for contextual video recognition.

Main Methods:

  • Proposed Adaptive Detrending (AD) for temporal normalization in recurrent neural networks (RNNs).
  • AD identifies and subtracts trending changes within sequences for each neuron, mitigating internal covariate shift.
  • Experiments utilized ConvGRUs for contextual video recognition tasks.

Main Results:

  • ConvGRUs demonstrated superior performance compared to feed-forward neural networks.
  • AD significantly accelerated training and enhanced generalization capabilities for ConvGRUs.
  • Coupling AD with other normalization methods further boosted performance.
  • The benefits of AD increased proportionally with the demand for long-term contextual information.

Conclusions:

  • Adaptive Detrending is an effective method for accelerating ConvRNN training in video recognition.
  • AD improves model generalization and performance, especially in tasks requiring extensive contextual understanding.
  • This technique offers a significant advantage for complex, long-term video analysis.