论文标题
视频中的密集活动事件：SYSU提交活动网络挑战2020
Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
论文作者
论文摘要
该技术报告简要说明了我们对2020年活动网络挑战的密集视频字幕任务的提交。我们的方法遵循两阶段的管道：首先，我们提取一组时间事件建议；然后，我们提出了一个多事件字幕模型，以捕获事件级的时间关系并有效融合多模式信息。我们的方法在测试集中达到了9.28的流星得分。
This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relationships and effectively fuse the multi-modal information. Our approach achieves a 9.28 METEOR score on the test set.
