Based on Spatial and Temporal Implicit Semantic Relational Inference for Cross-Modal Retrieval

To meet users’ demands for video retrieval, text-video cross-modal retrieval technology continues to evolve. Methods based on pre-trained models and transfer learning are widely employed in designing cross-modal retrieval models, significantly enhancing the accuracy of video retrieval. However, these methods exhibit shortcomings when it comes to studying the relationships between video frames, preventing the model from fully establishing the hidden semantic relationships within video features. To further deduce the implicit semantic relationships among video frames, we propose a cross-modal retrieval model based on graph convolutional networks (GCN) and visual semantic inference (GVSI). The GCN is utilized to establish relationships between video frame features, facilitating the mining of hidden semantic information across video frames. In order to use text semantic features to help the model to infer temporal and implicit semantic information between video frames, we introduce a semantic mining and temporal space (SM&TS) inference module. Additionally, we design semantic alignment modules (SA_M) to align explicit and implicit object features present in both video and text. Finally, we analyze and validate the effectiveness of the model using MSR-VTT, MSVD, and LSMDC datasets. IEEE