Summary of Edsnet: Efficient-dsnet For Video Summarization, by Ashish Prasad et al.
EDSNet: Efficient-DSNet for Video Summarization
by Ashish Prasad, Pranav Jeevan, Amit Sethi
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to video summarization is proposed in this research paper, which aims to enhance the efficiency of existing transformer-based architectures. The Direct-to-Summarize Network (DSNet) is modified by replacing traditional attention mechanisms with more resource-efficient alternatives like Fourier and Wavelet transforms, as well as Nyströmformer. Additionally, various pooling strategies are explored within the Regional Proposal Network, including ROI pooling, Fast Fourier Transform pooling, and flat pooling. Experimental results on TVSum and SumMe datasets demonstrate that these modifications significantly reduce computational costs while maintaining competitive summarization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to make video summarization faster and more efficient. Researchers took a popular approach called the Direct-to-Summarize Network (DSNet) and made it better by using new techniques like Fourier and Wavelet transforms, which are more efficient than what was used before. They also tried different ways of combining information from different parts of a video to get a summary. The results showed that these changes make summarization faster without sacrificing quality. |
Keywords
» Artificial intelligence » Attention » Summarization » Transformer
