Video Moment Retrieval (VMR) is a task to localize the temporal moment in
untrimmed video specified by natural language query. For VMR, several methods
that require full supervision for training have been proposed. Unfortunately,
acquiring a large number of training videos with labeled temporal boundaries
for each query is a labor-intensive process. This paper explores methods for
performing VMR in a weakly-supervised manner (wVMR): training is performed
without temporal moment labels but only with the text query that describes a
segment of the video. Existing methods on wVMR generate multi-scale proposals
and apply query-guided attention mechanisms to highlight the most relevant
proposal. To leverage the weak supervision, contrastive learning is used which
predicts higher scores for the correct video-query pairs than for the incorrect
pairs. It has been observed that a large number of candidate proposals, coarse
query representation, and one-way attention mechanism lead to blurry attention
maps which limit the localization performance. To handle this issue,
Video-Language Alignment Network (VLANet) is proposed that learns sharper
attention by pruning out spurious candidate proposals and applying a
multi-directional attention mechanism with fine-grained query representation.
The Surrogate Proposal Selection module selects a proposal based on the
proximity to the query in the joint embedding space, and thus substantially
reduces candidate proposals which leads to lower computation load and sharper
attention. Next, the Cascaded Cross-modal Attention module considers dense
feature interactions and multi-directional attention flow to learn the
multi-modal alignment. VLANet is trained end-to-end using contrastive loss
which enforces semantically similar videos and queries to gather. The
experiments show that the method achieves state-of-the-art performance on
Charades-STA and DiDeMo datasets.

本文提出了一种弱监督方法（wVMR）进行视频瞬间检索，通过使用对齐网络实现了更清晰的注意力和更少的计算负荷，并使用对比学习来训练模型。