Novel cross-dimensional coarse-fine-grained complementary network for image-text matching.

ABSTRACT: The fundamental aspects of multimodal applications such as image-text matching, and cross-modal heterogeneity gap between images and texts have always been challenging and complex. Researchers strive to overcome the challenges by proposing numerous significant efforts directed toward narrowing the semantic gap between visual and textual modalities. However, existing methods are usually limited to computing the similarity between images (image regions) and text (text words), ignoring the semantic consistency between fine-grained matching of word regions and coarse-grained overall matching of image and text. Additionally, these methods often ignore the semantic differences across different feature dimensions. Such limitations may result in an overemphasis on specific details at the expense o

SUBMITTER: Liu M 

PROVIDER: S-EPMC11888920 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature