EDSNet: Efficient-DSNet for Video Summarization Article Swipe

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A. K. S. K. Prasad , Pranav Jeevan , Amit Sethi ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.14724

Current video summarization methods largely rely on transformer-based architectures, which, due to their quadratic complexity, require substantial computational resources. In this work, we address these inefficiencies by enhancing the Direct-to-Summarize Network (DSNet) with more resource-efficient token mixing mechanisms. We show that replacing traditional attention with alternatives like Fourier, Wavelet transforms, and Nyströmformer improves efficiency and performance. Furthermore, we explore various pooling strategies within the Regional Proposal Network, including ROI pooling, Fast Fourier Transform pooling, and flat pooling. Our experimental results on TVSum and SumMe datasets demonstrate that these modifications significantly reduce computational costs while maintaining competitive summarization performance. Thus, our work offers a more scalable solution for video summarization tasks.

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Computer Science

Artificial Intelligence

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Automatic summarization Computer science Artificial intelligence

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.14724
PDF: https://arxiv.org/pdf/2409.14724
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4403780263

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OpenAlex ID: https://openalex.org/W4403780263

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DOI: https://doi.org/10.48550/arxiv.2409.14724

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Title: EDSNet: Efficient-DSNet for Video Summarization

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-23

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Authors: A. K. S. K. Prasad, Pranav Jeevan, Amit Sethi

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Landing page: https://arxiv.org/abs/2409.14724

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PDF URL: https://arxiv.org/pdf/2409.14724

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2409.14724

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Concepts: Automatic summarization, Computer science, Artificial intelligence

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Cited by: 0

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Related works (count): 10

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