Image-text retrieval is a fundamental and vital task in multi-media retrieval and has received growing attention since it connects heterogeneous data. Previous methods that perform well on image-text retrieval mainly focus on the interaction between image regions and text words. But these approaches lack joint exploration of characteristics and contexts of regions and words, which will cause semantic confusion of similar objects and loss of contextual understanding. To address these issues, a dual-level representation enhancement network (DREN) is proposed to strength the characteristic and contextual representations by innovative block-level and instance-level representation enhancement modules, respectively. The block-level module focuses on mining the potential relations between multiple blocks within each instance representation, while the instance-level module concentrates on learning the contextual relations between different instances. To facilitate the accurate matching of image-text pairs, we propose the graph correlation inference and weighted adaptive filtering to conduct the local and global matching between image-text pairs. Extensive experiments on two challenging datasets (i.e., Flickr30K and MSCOCO) verify the superiority of our method for image-text retrieval.

Image-text retrieval, as a fundamental task in the cross-modal field, aims to explore the relationship between visual and textual modalities. Recent methods address this task only by learning the conceptual and syntactical correspondences between cross-modal fragments, but these correspondences inevitably contain noise without considering external knowledge. To solve this issue, we propose a novel C ommonsense-Guided S emantic and R elational C onsistencies (CSRC) for image-text retrieval that can simultaneously expand the semantics and relations to reduce the cross-modal differences under the assumption that the semantics and relations of the true image-text pair should be consistent between two modalities. Specifically, we first explore commonsense knowledge to expand the specific concepts for visual and textual graphs and optimize the semantic consistency by minimizing the differences in cross-modal semantic importance. Then, we extend the same relations for cross-modal concept pairs with semantic consistency, which serves to implement relational consistency. After that, we combine external commonsense knowledge with internal correlation to enhance concept representation and further optimize relational consistency by regularizing the importance differences between association-enhanced concepts. Extensive experimental results on two popular image-text retrieval datasets demonstrate the effectiveness of our proposed method.

Image–text retrieval is a vital task in computer vision and has received growing attention, since it connects cross-modality data. It comes with the critical challenges of learning unified representations and eliminating the large gap between visual and textual domains. Over the past few decades, although many works have made significant progress in image–text retrieval, they are still confronted with the challenge of incomplete text descriptions of images, i.e., how to fully learn the correlations between relevant region–word pairs with semantic diversity. In this article, we propose a novel semantic completion and filtration (SCAF) method to alleviate the above issue. Specifically, the text semantic completion module is presented to generate a complete semantic description of an image using multi-view text descriptions, guiding the model to explore the correlations of relevant region–word pairs fully. Meanwhile, the adaptive structural semantic matching module is presented to filter irrelevant region–word pairs by considering the relevance score of each region–word pair, which facilitates the model to focus on learning the relevance of matching pairs. Extensive experiments show that our SCAF outperforms the existing methods on Flickr30K and MSCOCO datasets, which demonstrates the superiority of our proposed method.