  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.
