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Convolutional-Block-Attention Dual Path Networks for Slide Transition Detection in Lecture Videos

  • Minhuang Guan
  • Kai Li
  • Ran MaEmail author
  • Ping An
Conference paper
  • 43 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1181)

Abstract

Slide transition detection is used to find the images where the slide content changes, which form a summary of the lecture video and save the time for watching the lecture videos. 3D Convolutional Networks (3D ConvNet) has been regarded as an efficient approach to learn spatio-temporal features in videos. However, 3D ConvNet gives the same weight to all features in the image, and can’t focus on key feature information. We solve this problem by using the attention mechanism, which highlights more effective features information by suppressing invalid ones. Furthermore, 3D ConvNet usually costs much training time and needs lots of memory. Dual Path Network (DPN) combines the two network structures of ResNext and DenseNet and has the advantages of them. ResNext adds input directly to the convolved output, which takes advantage of extracted features from the previous hierarchy. DenseNet concatenates the output of each layer to the input of each layer, which extracts new features from the previous hierarchy. Based on the two networks, DPN not only saves training time and memory, but also extracts more effective features and improves training results. Consequently, we present a novel ConvNet architecture based on Convolutional Block Attention and DPN for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better results than other slide detection approaches.

Keywords

Lecture video Slide transition 3D ConvNet Convolutional Block Attention DPN 

Notes

Acknowledgment

This work was supported by the Project of National Natural Science Foundation of (No. 61601278), “Chen Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (No. 17CG41).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghaiChina
  2. 2.School of Communication and Information EngineeringShanghai UniversityShanghaiChina

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