  Vision-to-language tasks aim to integrate computer vision and natural
language processing together, which has attracted the attention of many
researchers. For typical approaches, they encode image into feature
representations and decode it into natural language sentences. While they
neglect high-level semantic concepts and subtle relationships between image
regions and natural language elements. To make full use of these information,
this paper attempt to exploit the text guided attention and semantic-guided
attention (SA) to find the more correlated spatial information and reduce the
semantic gap between vision and language. Our method includes two level
attention networks. One is the text-guided attention network which is used to
select the text-related regions. The other is SA network which is used to
highlight the concept-related regions and the region-related concepts. At last,
all these information are incorporated to generate captions or answers.
Practically, image captioning and visual question answering experiments have
been carried out, and the experimental results have shown the excellent
performance of the proposed approach.
