In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-attention based network for video summarization which performs the entire sequence to sequence transformation in a single feed forward pass and single backward pass during training. Our method sets a new state of the art results on two benchmarks TvSum and SumMe, commonly used in this domain.
@article{arxiv.1812.01969,
title = {Summarizing Videos with Attention},
author = {Jiri Fajtl and Hajar Sadeghi Sokeh and Vasileios Argyriou and Dorothy Monekosso and Paolo Remagnino},
journal= {arXiv preprint arXiv:1812.01969},
year = {2019}
}