The task of temporally grounding textual queries in videos is to localize one
video segment that semantically corresponds to the given query. Most of the
existing approaches rely on segment-sentence pairs (temporal annotations) for
training, which are usually unavailable in real-world scenarios. In this work
we present an effective weakly-supervised model, named as Multi-Level
Attentional Reconstruction Network (MARN), which only relies on video-sentence
pairs during the training stage. The proposed method leverages the idea of
attentional reconstruction and directly scores the candidate segments with the
learnt proposal-level attentions. Moreover, another branch learning clip-level
attention is exploited to refine the proposals at both the training and testing
stage. We develop a novel proposal sampling mechanism to leverage
intra-proposal information for learning better proposal representation and
adopt 2D convolution to exploit inter-proposal clues for learning reliable
attention map. Experiments on Charades-STA and ActivityNet-Captions datasets
demonstrate the superiority of our MARN over the existing weakly-supervised
methods.

本文提出一种基于弱监督学习的多级注意力重建网络（MARN）来实现视频中文本查询的时间定位，该方法又利用注意重建的思想，建立了一个候选时间片段的注意力模型，并利用候选片段和视频句子对进行训练。作者还利用 2D 卷积来提取更可靠的注意力图，并在 Charades-STA 和 ActivityNet-Captions 数据集上进行实验，表明 MARN 优于现有的弱监督方法。