This technical report analyzes a temporal action localization method we used
in the HACS competition which is hosted in Activitynet Challenge 2020.The goal
of our task is to locate the start time and end time of the action in the
untrimmed video, and predict action category.Firstly, we utilize the
video-level feature information to train multiple video-level action
classification models. In this way, we can get the category of action in the
video.Secondly, we focus on generating high quality temporal proposals.For this
purpose, we apply BMN to generate a large number of proposals to obtain high
recall rates. We then refine these proposals by employing a cascade structure
network called Refine Network, which can predict position offset and new IOU
under the supervision of ground truth.To make the proposals more accurate, we
use bidirectional LSTM, Nonlocal and Transformer to capture temporal
relationships between local features of each proposal and global features of
the video data.Finally, by fusing the results of multiple models, our method
obtains 40.55% on the validation set and 40.53% on the test set in terms of
mAP, and achieves Rank 1 in this challenge.

本技术报告分析了我们在 Activitynet Challenge 2020 中使用的一种时间动作定位方法，该方法利用视频级别特征信息训练多个视频级别动作分类模型，并应用 BMN 等方法产生高质量的时间提案，再通过级联结构网络 Refine Network 和多种模型融合方法，实现了在 HACS 比赛中 Rank1 的好成绩。