HIERARCHICAL MODEL FOR LONG-LENGTH VIDEO SUMMARIZATION WITH ADVERSARIALLY ENHANCED AUDIO/VISUAL FEATURES

In this paper, we propose a novel supervised method for summarizing long-length videos. Many recent approaches presented promising results in video summarization. However, videos in most benchmark datasets are short in duration (<10 minutes), and the methods often do not work well for very long-length videos (>1 hour). Furthermore, most approaches only use visual features, while audios provide useful information for the task. Based on these observations, we present a model that exploits both audio and visual features. To handle long videos, the hierarchical structure of our model captures both the short-term and long-term temporal dependencies. Our model also refines the extracted features using adversarial networks. To demonstrate our model, we have collected a new dataset of 28 baseball (~3.5 hours) videos, accompanied by an editorial summary video that is 5% in length of the original video. Evaluation on the dataset suggests that our method produces quality summaries for very long videos.