Name
Abstract | ||
|---|---|---|
This paper focuses on fully understanding the figure skating sport videos. In particular, we present a large-scale figure skating sport video dataset, which include 500 figure skating videos. On average, the length of each video is 2 minute and 50 seconds. Each video is annotated by three scores from nine different referees, i.e., Total Element Score(TES), Total Program Component Score (PCS), and Total Deductions(DED). The players of this dataset come from more than 20 different countries. We compare different features and models to predict the scores of each video. We also derive a video summarization dataset of 476 videos with the ground-truth video summary produced from the great shot. A reinforcement learning based video summarization algorithm is proposed here; and the experiments show better performance than the other baseline video summarization algorithms. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Multimedia | Automatic summarization,Computer science,Natural language processing,Artificial intelligence,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1802.02774 | 1 |
PageRank | References | Authors |
0.36 | 36 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Zhang | 1 | 3 | 1.09 |
| Chengming Xu | 2 | 1 | 0.36 |
| Changmao Cheng | 3 | 23 | 2.09 |
| Yanwei Fu | 4 | 543 | 51.93 |
| Yu-Gang Jiang | 5 | 3071 | 152.58 |
| Xiangyang Xue | 6 | 2466 | 154.25 |