  Image-Text Matching is one major task in cross-modal information processing.
The main challenge is to learn the unified visual and textual representations.
Previous methods that perform well on this task primarily focus on not only the
alignment between region features in images and the corresponding words in
sentences, but also the alignment between relations of regions and relational
words. However, the lack of joint learning of regional features and global
features will cause the regional features to lose contact with the global
context, leading to the mismatch with those non-object words which have global
meanings in some sentences. In this work, in order to alleviate this issue, it
is necessary to enhance the relations between regions and the relations between
regional and global concepts to obtain a more accurate visual representation so
as to be better correlated to the corresponding text. Thus, a novel multi-level
semantic relations enhancement approach named Dual Semantic Relations Attention
Network(DSRAN) is proposed which mainly consists of two modules, separate
semantic relations module and the joint semantic relations module. DSRAN
performs graph attention in both modules respectively for region-level
relations enhancement and regional-global relations enhancement at the same
time. With these two modules, different hierarchies of semantic relations are
learned simultaneously, thus promoting the image-text matching process by
providing more information for the final visual representation. Quantitative
experimental results have been performed on MS-COCO and Flickr30K and our
method outperforms previous approaches by a large margin due to the
effectiveness of the dual semantic relations learning scheme. Codes are
available at https://github.com/kywen1119/DSRAN.

Computer Vision and Pattern Recognition;Multimedia