Evaluating the Supervised Video Summarization Model VASNet on an Action Localization Dataset

Abstract

There is growing research on automated video summarization following the rise of video content. However, the subjectivity of the task itself is still an issue to address. This subjectivity stems from the fact that there can be different summaries for the same video depending on which parts one considers important. Supervised models especially suffer from this problem as they need informative labels to learn from. As a result, upon evaluation, supervised models appear to perform worse than unsupervised models. This inspired our research on whether action localization can aid the video summarization process. To investigate this issue, this paper will answer the question of how well VASNet, a supervised video summarization model, can predict summaries for videos in an action localization dataset. This involves investigating whether action localization can produce well-correlated human-generated summaries and how it affects the quality of predicted summaries. Our findings reveal that there is a positive indication that action localization can aid in producing more well-correlated human summaries. In addition, we have observed that upon comparison with several video summarization models, VASNet has performed well and that in general, supervised models appear to outperform unsupervised ones when trained with an action localization dataset.