Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a video as short summaries. Recently, a more generalized task, query-conditioned video summarization, has been introduced, which takes user queries into consideration to learn more user-oriented summaries. In this paper, we propose a query-conditioned three-player generative adversarial network to tackle this challenge. The generator learns the joint representation of the user query and the video content, and the discriminator takes three pairs of query-conditioned summaries as the input to discriminate the real summary from a generated and a random one. A three-player loss is introduced for joint training of the generator and the discriminator, which forces the generator to learn better summary results, and avoids the generation of random trivial summaries. Experiments on a recently proposed query-conditioned video summarization benchmark dataset show the efficiency and efficacy of our proposed method.

本文提出了一种查询驱动的三方生成对抗网络，通过学习用户查询和视频内容的联合表示，并引入三方损失函数，即鼓励生成器学习更好的摘要结果，避免生成随机无意义摘要。实验结果表明，该方法在一些查询驱动的视频摘要基准数据集上表现出了高效性和可行性。

基于查询条件的三人对抗网络在视频摘要中的应用