  Image-text matching plays a critical role in bridging the vision and
language, and great progress has been made by exploiting the global alignment
between image and sentence, or local alignments between regions and words.
However, how to make the most of these alignments to infer more accurate
matching scores is still underexplored. In this paper, we propose a novel
Similarity Graph Reasoning and Attention Filtration (SGRAF) network for
image-text matching. Specifically, the vector-based similarity representations
are firstly learned to characterize the local and global alignments in a more
comprehensive manner, and then the Similarity Graph Reasoning (SGR) module
relying on one graph convolutional neural network is introduced to infer
relation-aware similarities with both the local and global alignments. The
Similarity Attention Filtration (SAF) module is further developed to integrate
these alignments effectively by selectively attending on the significant and
representative alignments and meanwhile casting aside the interferences of
non-meaningful alignments. We demonstrate the superiority of the proposed
method with achieving state-of-the-art performances on the Flickr30K and MSCOCO
datasets, and the good interpretability of SGR and SAF modules with extensive
qualitative experiments and analyses.
