Many methods have been developed to help people find the video contents they
want efficiently. However, there are still some unsolved problems in this area.
For example, given a query video and a reference video, how to accurately
localize a segment in the reference video such that the segment semantically
corresponds to the query video? We define a distinctively new task, namely
\textbf{video re-localization}, to address this scenario. Video re-localization
is an important emerging technology implicating many applications, such as fast
seeking in videos, video copy detection, video surveillance, etc. Meanwhile, it
is also a challenging research task because the visual appearance of a semantic
concept in videos can have large variations. The first hurdle to clear for the
video re-localization task is the lack of existing datasets. It is labor
expensive to collect pairs of videos with semantic coherence or correspondence
and label the corresponding segments. We first exploit and reorganize the
videos in ActivityNet to form a new dataset for video re-localization research,
which consists of about 10,000 videos of diverse visual appearances associated
with localized boundary information. Subsequently, we propose an innovative
cross gated bilinear matching model such that every time-step in the reference
video is matched against the attentively weighted query video. Consequently,
the prediction of the starting and ending time is formulated as a
classification problem based on the matching results. Extensive experimental
results show that the proposed method outperforms the competing methods. Our
code is available at: this https URL

提出了视频重新本地化任务以解决语义相应问题，并构建一个新的数据集，基于跨门控双线性匹配模型对查询视频进行权重匹配，解决了开始和结束时间的分类问题，在视频监控等多个领域具有重要应用。