Video retrieval is a challenging research topic bridging the vision and
language areas and has attracted broad attention in recent years. Previous
works have been devoted to representing videos by directly encoding from
frame-level features. In fact, videos consist of various and abundant semantic
relations to which existing methods pay less attention. To address this issue,
we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit
reasoning between frame regions. Specifically, we consider frame regions as
vertices and construct a fully-connected semantic correlation graph. Then, we
perform reasoning by novel random walk rule-based graph convolutional networks
to generate region features involved with semantic relations. With the benefit
of reasoning, semantic interactions between regions are considered, while the
impact of redundancy is suppressed. Finally, the region features are aggregated
to form frame-level features for further encoding to measure video-text
similarity. Extensive experiments on two public benchmark datasets validate the
effectiveness of our method by achieving state-of-the-art performance due to
the powerful semantic reasoning.

为了提高视频检索的性能，我们提出了一种名为 ViSERN 的可视化语义增强的推理网络，该网络利用图卷积网络执行随机游走规则来生成涉及语义关系的区域特征，并聚合这些特征以形成帧级特征， 以求衡量视频和文本之间的相似性。