Evaluation of Video Summarization using DSNet and Action Localization Datasets

Abstract

In this paper, the DSNet framework used for automatic video summarization gets reviewed when using action localization datasets. The problem facing video summarizations using deep learning techniques is that datasets can be subjective depending on preferences of human annotators, making for noise in the labeling. This paper will look at a anchor-based approach and anchor-free approach which were introduced by the DSNet framework. More specific it will evaluate in experiments using different hyper-parameters if these approaches gain an increased performances when using action localization datasets instead. These results will show the increase in accuracy when using action localization datasets. Moreover it will compare the different approaches, meaning anchor-based and anchor-free, and see if they still have comparable performance with the method.