CFCM: a cascading fusion cross-attention model for visual textual retrieval

The rapid growth in digital content has necessitated advanced retrieval systems capable of efficiently managing and searching through multimodal data. This paper introduces the Cascading Fusion Cross-attention Model for Visual Textual Retrieval, a novel approach designed to enhance the accuracy of retrieving textual descriptions corresponding to visual data and vice versa. The model employs a unique cascading fusion strategy that integrates multiple levels of feature representations from both modalities, ensuring a comprehensive understanding of the content. Simultaneously, a cross-attention mechanism dynamically aligns these features, focusing on the semantic correlations between the visual and textual modalities. Through extensive experiments and comparisons with existing state-of-the-art models, the proposed method demonstrates superior performance in terms of recall. The findings suggest that the Cascading Fusion Cross-attention Model holds great promise for advancing the field of multimodal retrieval.