Automatically narrating a video with natural language can assist people in
grasping and managing massive videos on the Internet. From the perspective of
video uploaders, they may have varied preferences for writing the desired video
description to attract more potential followers, e.g. catching customers'
attention for product videos. The Controllable Video Captioning task is
therefore proposed to generate a description conditioned on the user demand and
video content. However, existing works suffer from two shortcomings: 1) the
control signal is fixed and can only express single-grained control; 2) the
video description can not be further edited to meet dynamic user demands. In
this paper, we propose a novel Video Description Editing (VDEdit) task to
automatically revise an existing video description guided by flexible user
requests. Inspired by human writing-revision habits, we design the user command
as a {operation, position, attribute} triplet to cover multi-grained use
requirements, which can express coarse-grained control (e.g. expand the
description) as well as fine-grained control (e.g. add specified details in
specified position) in a unified format. To facilitate the VDEdit task, we
first automatically construct a large-scale benchmark dataset namely VATEX-EDIT
in the open domain describing diverse human activities. Considering the
real-life application scenario, we further manually collect an e-commerce
benchmark dataset called EMMAD-EDIT. We propose a unified framework to convert
the {operation, position, attribute} triplet into a textual control sequence to
handle multi-grained editing commands. For VDEdit evaluation, we adopt
comprehensive metrics to measure three aspects of model performance, including
caption quality, caption-command consistency, and caption-video alignment.

本文提出了一个新的视频描述编辑任务（VDEdit），旨在通过灵活的用户需求自动修订现有的视频描述。为支持 VDEdit 任务，我们首先自动构建了一个大规模基准数据集 VATEX-EDIT，描述了不同的人类活动。考虑到现实生活中的应用场景，我们还手工收集了一个电子商务基准数据集 EMMAD-EDIT。我们提出了一个统一的框架，将操作，位置，属性三元组转化为文本控制序列以处理多粒度的编辑命令，并采用综合性指标评估 VDEdit 功能。