This paper aims to tackle a novel task - Temporal Sentence Grounding in
Streaming Videos (TSGSV). The goal of TSGSV is to evaluate the relevance
between a video stream and a given sentence query. Unlike regular videos,
streaming videos are acquired continuously from a particular source, and are
always desired to be processed on-the-fly in many applications such as
surveillance and live-stream analysis. Thus, TSGSV is challenging since it
requires the model to infer without future frames and process long historical
frames effectively, which is untouched in the early methods. To specifically
address the above challenges, we propose two novel methods: (1) a TwinNet
structure that enables the model to learn about upcoming events; and (2) a
language-guided feature compressor that eliminates redundant visual frames and
reinforces the frames that are relevant to the query. We conduct extensive
experiments using ActivityNet Captions, TACoS, and MAD datasets. The results
demonstrate the superiority of our proposed methods. A systematic ablation
study also confirms their effectiveness.

该研究旨在解决一项新颖任务 —— 流媒体视频中的时间句子定位，通过提出 TwinNet 结构和语言引导的特征压缩器，该方法在多个数据集上进行了广泛实验证明了其卓越性能。