# SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2202.03540 · HTML: https://mlsanity.com/paper/2202.03540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Dual Path Networks](https://mlsanity.com/paper/1707.01629/) (2017)
- [You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization](https://mlsanity.com/paper/1911.06644/) (2019)
- [TransNet: A deep network for fast detection of common shot transitions](https://mlsanity.com/paper/1906.03363/) (2019)
- [Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks](https://mlsanity.com/paper/1705.03281/) (2017)

## Abstract (arXiv)

With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming. Therefore, automatic slide extraction from the lecture videos can be helpful to give a brief overview of the main content and to support the students in their studies. For this task, we propose a deep learning method to detect slide transitions in lectures videos. We first process each frame of the video by a heuristic-based approach using a 2-D convolutional neural network to predict transition candidates. Then, we increase the complexity by employing two 3-D convolutional neural networks to refine the transition candidates. Evaluation results demonstrate the effectiveness of our method in finding slide transitions.