  Image-text matching tasks have recently attracted a lot of attention in the
computer vision field. The key point of this cross-domain problem is how to
accurately measure the similarity between the visual and the textual contents,
which demands a fine understanding of both modalities. In this paper, we
propose a novel position focused attention network (PFAN) to investigate the
relation between the visual and the textual views. In this work, we integrate
the object position clue to enhance the visual-text joint-embedding learning.
We first split the images into blocks, by which we infer the relative position
of region in the image. Then, an attention mechanism is proposed to model the
relations between the image region and blocks and generate the valuable
position feature, which will be further utilized to enhance the region
expression and model a more reliable relationship between the visual image and
the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO
show the effectiveness of the proposed method. Besides the public datasets, we
also conduct experiments on our collected practical large-scale news dataset
(Tencent-News) to validate the practical application value of proposed method.
As far as we know, this is the first attempt to test the performance on the
practical application. Our method achieves the state-of-art performance on all
of these three datasets.
