Weakly Supervised Deep Reinforcement Learning for Video Summarization With Semantically Meaningful Reward

Abstract

Conventional unsupervised video summarization algorithms are usually developed in a frame level clustering manner For example, frame level diversity and representativeness are two typical clustering criteria used for unsupervised reinforcement learning-based video summarization. Inspired by recent progress in video representation techniques, we further introduce the similarity of video representations to construct a semantically meaningful reward for this task. We consider that a good summarization should also be semantically identical to its original source, which means that the semantic similarity can be regarded as an additional criterion for summarization. Through combining a novel video semantic reward with other unsupervised rewards for training, we can easily upgrade an unsupervised reinforcement learning-based video summarization method to its weakly supervised version. In practice, we first train a video classification sub-network (VCSN) to extract video semantic representations based on a category-labeled video dataset. Then we fix this VCSN and train a summary generation sub-network (SGSN) using unlabeled video data in a reinforcement learning way. Experimental results demonstrate that our work significantly surpasses other unsupervised and even supervised methods. To the best of our knowledge, our method achieves state-of-the-art performance in terms of the correlation coefficients, Kendall's and Spearman's p.