Localizing moments in a longer video via natural language queries is a new,
challenging task at the intersection of language and video understanding.
Though moment localization with natural language is similar to other language
and vision tasks like natural language object retrieval in images, moment
localization offers an interesting opportunity to model temporal dependencies
and reasoning in text. We propose a new model that explicitly reasons about
different temporal segments in a video, and shows that temporal context is
important for localizing phrases which include temporal language. To benchmark
whether our model, and other recent video localization models, can effectively
reason about temporal language, we collect the novel TEMPOral reasoning in
video and language (TEMPO) dataset. Our dataset consists of two parts: a
dataset with real videos and template sentences (TEMPO - Template Language)
which allows for controlled studies on temporal language, and a human language
dataset which consists of temporal sentences annotated by humans (TEMPO - Human
Language).

本文提出了一种新模型，该模型可以明确地推理视频中的不同时间片段，证明了在包括时间语言的短语定位中，时间上下文的重要性，并通过 TEMPO 数据集进行了实验以验证模型可靠性。