Video-text retrieval is an important yet challenging task in vision-language
understanding, which aims to learn a joint embedding space where related video
and text instances are close to each other. Most current works simply measure
the video-text similarity based on video-level and text-level embeddings.
However, the neglect of more fine-grained or local information causes the
problem of insufficient representation. Some works exploit the local details by
disentangling sentences, but overlook the corresponding videos, causing the
asymmetry of video-text representation. To address the above limitations, we
propose a Hierarchical Alignment Network (HANet) to align different level
representations for video-text matching. Specifically, we first decompose video
and text into three semantic levels, namely event (video and text), action
(motion and verb), and entity (appearance and noun). Based on these, we
naturally construct hierarchical representations in the individual-local-global
manner, where the individual level focuses on the alignment between frame and
word, local level focuses on the alignment between video clip and textual
context, and global level focuses on the alignment between the whole video and
text. Different level alignments capture fine-to-coarse correlations between
video and text, as well as take the advantage of the complementary information
among three semantic levels. Besides, our HANet is also richly interpretable by
explicitly learning key semantic concepts. Extensive experiments on two public
datasets, namely MSR-VTT and VATEX, show the proposed HANet outperforms other
state-of-the-art methods, which demonstrates the effectiveness of hierarchical
representation and alignment. Our code is publicly available.

本文提出了一种 Hierarchical Alignment Network 框架，通过把视频和文本分解成三个层次，即事件 - 动作 - 实体水平，并在个体 - 局部 - 全局层次上构建层次表示，捕捉视频和文本之间的从精细到粗略的对应关系，从而最大化利用三个语义层次的互补信息，以实现视频文本检索和匹配任务的高效和精度。实验结果表明，所提出的框架在两个公共数据集上优于其他最先进的方法，证明了层次表示和匹配的有效性。