Text-video retrieval is a challenging task that aims to search relevant video
contents based on natural language descriptions. The key to this problem is to
measure text-video similarities in a joint embedding space. However, most
existing methods only consider the global cross-modal similarity and overlook
the local details. Some works incorporate the local comparisons through
cross-modal local matching and reasoning. These complex operations introduce
tremendous computation. In this paper, we design an efficient global-local
alignment method. The multi-modal video sequences and text features are
adaptively aggregated with a set of shared semantic centers. The local
cross-modal similarities are computed between the video feature and text
feature within the same center. This design enables the meticulous local
comparison and reduces the computational cost of the interaction between each
text-video pair. Moreover, a global alignment method is proposed to provide a
global cross-modal measurement that is complementary to the local perspective.
The global aggregated visual features also provide additional supervision,
which is indispensable to the optimization of the learnable semantic centers.
We achieve consistent improvements on three standard text-video retrieval
benchmarks and outperform the state-of-the-art by a clear margin.

本文主要设计了一种高效的全局 - 局部对齐方法，在共享的语义中心集合中，自适应地汇聚多模式视频序列和文本特征，并计算相同中心内的视频特征与文本特征之间的局部交叉模态相似性，从而实现了细致的局部比较，同时降低了交互成本。此外，还提出了一种全局对齐方法，并在三个标准的文本 - 视频检索基准测试中取得了一致的改进和领先的效果。