Video summarization aims to generate a concise representation of a video,
capturing its essential content and key moments while reducing its overall
length. Although several methods employ attention mechanisms to handle
long-term dependencies, they often fail to capture the visual significance
inherent in frames. To address this limitation, we propose a CNN-based
SpatioTemporal Attention (CSTA) method that stacks each feature of frames from
a single video to form image-like frame representations and applies 2D CNN to
these frame features. Our methodology relies on CNN to comprehend the inter and
intra-frame relations and to find crucial attributes in videos by exploiting
its ability to learn absolute positions within images. In contrast to previous
work compromising efficiency by designing additional modules to focus on
spatial importance, CSTA requires minimal computational overhead as it uses CNN
as a sliding window. Extensive experiments on two benchmark datasets (SumMe and
TVSum) demonstrate that our proposed approach achieves state-of-the-art
performance with fewer MACs compared to previous methods. Codes are available
at this https URL

提出了一种基于 CNN 的时空注意力（CSTA）方法，将视频的每个帧的特征堆叠起来形成类似图像的帧表示，并应用 2D CNN 对这些帧特征进行处理，实现对关键属性的学习和视觉重要性的捕捉，在 SumMe 和 TVSum 等数据集上实验证明了该方法在减少计算量的同时取得了最先进的性能。