Effective Video Summarization by Extracting Parameter-Free Motion Attention
Article No.: 219, Pages 1 - 20
Abstract
Video summarization remains a challenging task despite increasing research efforts. Traditional methods focus solely on long-range temporal modeling of video frames, overlooking important local motion information that cannot be captured by frame-level video representations. In this article, we propose the Parameter-free Motion Attention Module (PMAM) to exploit the crucial motion clues potentially contained in adjacent video frames, using a multi-head attention architecture. The PMAM requires no additional training for model parameters, leading to an efficient and effective understanding of video dynamics. Moreover, we introduce the Multi-feature Motion Attention Network (MMAN), integrating the PMAM with local and global multi-head attention based on object-centric and scene-centric video representations. The synergistic combination of local motion information, extracted by the proposed PMAM, with long-range interactions modeled by the local and global multi-head attention mechanism, can significantly enhance the performance of video summarization. Extensive experimental results on the benchmark datasets, SumMe and TVSum, demonstrate that the proposed MMAN outperforms other state-of-the-art methods, resulting in remarkable performance gains.
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- Effective Video Summarization by Extracting Parameter-Free Motion Attention
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Publication History
Published: 16 May 2024
Online AM: 30 March 2024
Accepted: 20 March 2024
Revised: 19 March 2024
Received: 18 September 2023
Published in TOMM Volume 20, Issue 7
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