While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse space shared between the video and the text for video-text retrieval. The shared sparse space is initialized with a finite number of sparse concepts, each of which refers to a number of words. With the text data at hand, we learn and update the shared sparse space in a supervised manner using the proposed similarity and alignment losses. Moreover, to enable multi-grained alignment, we incorporate frame representations for better modeling the video modality and calculating fine-grained and coarse-grained similarities. Benefiting from the learned shared sparse space and multi-grained similarities, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of S3MA over existing methods. Our code is available at https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval.

翻译：尽管近期视频-文本检索领域通过探索更优表征学习取得了进展，本文提出了一种新颖的多粒度稀疏学习框架S3MA，旨在为视频与文本之间的对齐学习建立共享稀疏空间，并用于视频-文本检索。该共享稀疏空间通过有限数量的稀疏概念进行初始化，每个概念对应若干词汇。基于现有文本数据，我们利用所提出的相似度损失与对齐损失，以监督方式学习并更新该共享稀疏空间。此外，为实现多粒度对齐，我们引入帧级表示以更优地建模视频模态，并计算细粒度与粗粒度相似度。得益于所学习的共享稀疏空间与多粒度相似性，在多个视频-文本检索基准上的大量实验表明，S3MA相较于现有方法具有显著优越性。我们的代码已开源至https://github.com/yimuwangcs/Better_Cross_Modal_Retrieval。