In this paper we tackle the cross-modal video retrieval problem and, more
specifically, we focus on text-to-video retrieval. We investigate how to
optimally combine multiple diverse textual and visual features into feature
pairs that lead to generating multiple joint feature spaces, which encode
text-video pairs into comparable representations. To learn these
representations our proposed network architecture is trained by following a
multiple space learning procedure. Moreover, at the retrieval stage, we
introduce additional softmax operations for revising the inferred query-video
similarities. Extensive experiments in several setups based on three
large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to
best combine text-visual features and document the performance of the proposed
network. Source code is made publicly available at:
this https URL

本文旨在解决跨模态视频检索问题，具体聚焦于文本到视频的检索，并探讨将多种不同的文本和视觉特征最佳组合以生成多个联合特征空间的方法。通过多空间学习过程训练网络结构，引入额外的 softmax 运算来修正推断的查询 - 视频相似性，并在三个大规模数据集上进行实验验证，以记录所提出网络的表现。