FullTransNet: Full Transformer with Local-Global Attention for Video Summarization

• URL: http://arxiv.org/abs/2501.00882v2
• Date: Thu, 07 Aug 2025 10:00:01 GMT
• Title: FullTransNet: Full Transformer with Local-Global Attention for Video Summarization
• Authors: Libin Lan, Lu Jiang, Tianshu Yu, Xiaojuan Liu, Zhongshi He,
• Abstract summary: We propose a transformer-like architecture named FullTransNet for video summarization.<n>It uses a full transformer with an encoder-decoder structure as an alternative architecture for video summarization.<n>Our model achieves F-scores of 54.4% and 63.9%, respectively, while maintaining relatively low computational and memory requirements.
• Score: 16.134118247239527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video summarization aims to generate a compact, informative, and representative synopsis of raw videos, which is crucial for browsing, analyzing, and understanding video content. Dominant approaches in video summarization primarily rely on recurrent or convolutional neural networks, and more recently on encoder-only transformer architectures. However, these methods typically suffer from several limitations in parallelism, modeling long-range dependencies, and providing explicit generative capabilities. To address these issues, we propose a transformer-like architecture named FullTransNet with two-fold ideas. First, it uses a full transformer with an encoder-decoder structure as an alternative architecture for video summarization. As the full transformer is specifically designed for sequence transduction tasks, its direct application to video summarization is both intuitive and effective. Second, it replaces the standard full attention mechanism with a combination of local and global sparse attention, enabling the model to capture long-range dependencies while significantly reducing computational costs. This local-global sparse attention is applied exclusively at the encoder side, where the majority of computations occur, further enhancing efficiency. Extensive experiments on two widely used benchmark datasets, SumMe and TVSum, demonstrate that our model achieves F-scores of 54.4% and 63.9%, respectively, while maintaining relatively low computational and memory requirements. These results surpass the second-best performing methods by 0.1% and 0.3%, respectively, verifying the effectiveness and efficiency of FullTransNet.

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