Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one. Most of the existing video summarization approaches focus on hand-crafted labels. As the number of videos grows exponentially, there emerges an increasing need for methods that can learn meaningful summarizations without labeled annotations. In this paper, we aim to maximally exploit unsupervised video summarization while concentrating the supervision to a few, personalized labels as an add-on. To do so, we formulate the key requirements for the informative video summarization. Then, we propose contrastive learning as the answer to both questions. To further boost Contrastive video Summarization (CSUM), we propose to contrast top-k features instead of a mean video feature as employed by the existing method, which we implement with a differentiable top-k feature selector. Our experiments on several benchmarks demonstrate, that our approach allows for meaningful and diverse summaries when no labeled data is provided.

翻译：视频摘要旨在从视频中选取能够尽可能还原原始叙述故事的部分。现有大多数视频摘要方法依赖于手工标注的标签。随着视频数量呈指数级增长，对无需标注注释即可学习有意义的摘要方法的需求日益迫切。在本文中，我们旨在最大限度地利用无监督视频摘要，同时将监督集中在少量个性化标签上作为补充。为此，我们首先对信息性视频摘要所需的关键要求进行形式化定义。随后，我们提出以对比学习作为这两个问题的解决方案。为进一步提升对比视频摘要（CSUM）的性能，我们建议对top-k特征进行对比，而非采用现有方法所使用的平均视频特征，并通过可微分的top-k特征选择器实现这一方案。在多个基准测试上的实验表明，当未提供标注数据时，我们的方法能够生成有意义且多样化的摘要。