Multilevel Semantic Interaction Alignment for Video-Text Cross-Modal Retrieval

Video-text cross-modal retrieval (VTR) is more natural and challenging than image-text retrieval, which has attracted increasing interest from researchers in recent years. To align VTR more closely with real-world scenarios, i.e., weak semantic text description as a query, we propose a multilevel semantic interaction alignment (MSIA) model. We develop a two-stream network, which decomposes video and text alignment into multiple dimensions. Specifically, in the video stream, to better align heterogeneity data, redundant video information is suppressed via the designed frame adaptation attention mechanism, and richer semantic interaction is achieved through a text-guided attention mechanism. Then, for text alignment in the video local region, we design a distinctive anchor frame strategy and a word selection method. Finally, a cross-granularity alignment approach is designed to learn more and finer semantic features. With the above schema, the alignment between video and weak semantic text descriptions is reinforced, further alleviating the issues of difficult alignment caused by weak semantic text descriptions. The experimental results on VTR benchmark datasets show the competitive performance of our approach in comparison to that of state-of-the-art methods. The code is available at: https://github.com/jiaranjintianchism/MSIA.