  Exploring fine-grained relationship between entities(e.g. objects in image or
words in sentence) has great contribution to understand multimedia content
precisely. Previous attention mechanism employed in image-text matching either
takes multiple self attention steps to gather correspondences or uses image
objects (or words) as context to infer image-text similarity. However, they
only take advantage of semantic information without considering that objects'
relative position also contributes to image understanding. To this end, we
introduce a novel position-aware relation module to model both the semantic and
spatial relationship simultaneously for image-text matching in this paper.
Given an image, our method utilizes the location of different objects to
capture spatial relationship innovatively. With the combination of semantic and
spatial relationship, it's easier to understand the content of different
modalities (images and sentences) and capture fine-grained latent
correspondences of image-text pairs. Besides, we employ a two-step aggregated
relation module to capture interpretable alignment of image-text pairs. The
first step, we call it intra-modal relation mechanism, in which we computes
responses between different objects in an image or different words in a
sentence separately; The second step, we call it inter-modal relation
mechanism, in which the query plays a role of textual context to refine the
relationship among object proposals in an image. In this way, our
position-aware aggregated relation network (ParNet) not only knows which
entities are relevant by attending on different objects (words) adaptively, but
also adjust the inter-modal correspondence according to the latent alignments
according to query's content. Our approach achieves the state-of-the-art
results on MS-COCO dataset.
