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。

时间融合网络用于时序动作定位：提交至 ActivityNet Challenge 2020（E 任务）

Temporal Fusion Network for Temporal Action Localization:Submission to  ActivityNet Challenge 2020 (Task E)

In this work, we focus on the task of Automatic Question Generation (AQG)
where given a passage and an answer the task is to generate the corresponding
question. It is desired that the generated question should be (i) grammatically
correct (ii) answerable from the passage and (iii) specific to the given
answer. An analysis of existing AQG models shows that they produce questions
which do not adhere to one or more of {the above-mentioned qualities}. In
particular, the generated questions look like an incomplete draft of the
desired question with a clear scope for refinement. {To alleviate this
shortcoming}, we propose a method which tries to mimic the human process of
generating questions by first creating an initial draft and then refining it.
More specifically, we propose Refine Network (RefNet) which contains two
decoders. The second decoder uses a dual attention network which pays attention
to both (i) the original passage and (ii) the question (initial draft)
generated by the first decoder. In effect, it refines the question generated by
the first decoder, thereby making it more correct and complete. We evaluate
RefNet on three datasets, \textit{viz.}, SQuAD, HOTPOT-QA, and DROP, and show
that it outperforms existing state-of-the-art methods by 7-16\% on all of these
datasets. Lastly, we show that we can improve the quality of the second decoder
on specific metrics, such as, fluency and answerability by explicitly rewarding
revisions that improve on the corresponding metric during training. The code
has been made publicly available
\footnote{this https URL}

本文将重点放在自动生成问题 (AQG) 的任务上，同时提出了一种名为 Refine Network (RefNet) 的方法，其中包含两个解码器，第二个解码器使用了双重注意力网络，通过重视原始段落和第一个解码器生成的问题（即初始草稿）的注意力机制来对其进行修整，从而使其更为正确和完整，同时在 SQuAD、HOTPOT-QA 和 DROP 三个数据集上展示出了比现有的最先进方法在这些数据集上的表现高出 7-16％。