  Existing two-stream models for image-text matching show good performance
while ensuring retrieval speed and have received extensive attention from
industry and academia. These methods use a single representation to encode
image and text separately and get a matching score with cosine similarity or
the inner product of vectors. However, the performance of the two-stream model
is often sub-optimal. On the one hand, a single representation is challenging
to cover complex content comprehensively. On the other hand, in this framework
of lack of interaction, it is challenging to match multiple meanings which
leads to information being ignored. To address the problems mentioned above and
facilitate the performance of the two-stream model, we propose a multi-view
attention approach for two-stream image-text matching MVAM
(\textbf{M}ulti-\textbf{V}iew \textbf{A}ttention \textbf{M}odel). It first
learns multiple image and text representations by diverse attention heads with
different view codes. And then concatenate these representations into one for
matching. A diversity objective is also used to promote diversity between
attention heads. With this method, models are able to encode images and text
from different views and attend to more key points. So we can get
representations that contain more information. When doing retrieval tasks, the
matching scores between images and texts can be calculated from different
aspects, leading to better matching performance. Experiment results on MSCOCO
and Flickr30K show that our proposed model brings improvements over existing
models. Further case studies show that different attention heads can focus on
different contents and finally obtain a more comprehensive representation.
