Contextual reasoning is essential to understand events in long untrimmed
videos. In this work, we systematically explore different captioning models
with various contexts for the dense-captioning events in video task, which aims
to generate captions for different events in the untrimmed video. We propose
five types of contexts as well as two categories of event captioning models,
and evaluate their contributions for event captioning from both accuracy and
diversity aspects. The proposed captioning models are plugged into our pipeline
system for the dense video captioning challenge. The overall system achieves
the state-of-the-art performance on the dense-captioning events in video task
with 9.91 METEOR score on the challenge testing set.

本研究系统地探索了不同的视频字幕模型，提出了五种上下文和两类事件字幕模型，并从准确性和多样性两个角度评估了它们对事件字幕的贡献，该提出的字幕模型被插入到我们的管道系统中，本系统在视频任务的密集字幕事件上取得了最新颖的表现。

2019 年 Activitynet 第三项任务：探索视频中复杂事件的背景

Activitynet 2019 Task 3: Exploring Contexts for Dense Captioning Events  in Videos

This notebook paper presents an overview and comparative analysis of our
systems designed for the following five tasks in ActivityNet Challenge 2018:
temporal action proposals, temporal action localization, dense-captioning
events in videos, trimmed action recognition, and spatio-temporal action
localization.

本文总结和比较分析了我们针对 ActivityNet Challenge 2018 中的五项任务所设计的系统，包括时间动作提议、时间动作定位、视频中的密集事件字幕、裁剪动作识别和时空动作定位。

YH Technologies 在 ActivityNet Challenge 2018 中

YH Technologies at ActivityNet Challenge 2018

Most natural videos contain numerous events. For example, in a video of a
"man playing a piano", the video might also contain "another man dancing" or "a
crowd clapping". We introduce the task of dense-captioning events, which
involves both detecting and describing events in a video. We propose a new
model that is able to identify all events in a single pass of the video while
simultaneously describing the detected events with natural language. Our model
introduces a variant of an existing proposal module that is designed to capture
both short as well as long events that span minutes. To capture the
dependencies between the events in a video, our model introduces a new
captioning module that uses contextual information from past and future events
to jointly describe all events. We also introduce ActivityNet Captions, a
large-scale benchmark for dense-captioning events. ActivityNet Captions
contains 20k videos amounting to 849 video hours with 100k total descriptions,
each with it's unique start and end time. Finally, we report performances of
our model for dense-captioning events, video retrieval and localization.

本篇研究介绍了一种能够在视频中检测和描述事件的新模型，同时使用过去和未来事件的上下文信息来联合描述所有事件，并介绍了一个用于密集事件字幕的基准测试集 ActivityNet Captions。