Name
Abstract | ||
|---|---|---|
This notebook paper presents our system in the ActivityNet Dense Captioning in Video task (task 3). Temporal proposal generation and caption generation are both important to the dense captioning task. Therefore, we propose a proposal ranking model to employ a set of effective feature representations for proposal generation, and ensemble a series of caption models enhanced with context information to generate captions robustly on predicted proposals. Our approach achieves the state-of-the-art performance on the dense video captioning task with 8.529 METEOR score on the challenge testing set. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Challenge testing,Closed captioning,Ranking,Computer science,Natural language processing,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1806.08854 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shizhe Chen | 1 | 238 | 21.83 |
| Yuqing Song | 2 | 3 | 4.76 |
| Yida Zhao | 3 | 0 | 1.01 |
| Jiarong Qiu | 4 | 0 | 0.34 |
| Qin Jin | 5 | 639 | 66.86 |
| Alexander G. Hauptmann | 6 | 7472 | 558.23 |