  With the rapid growth of multimedia data, such as image and text, it is a
highly challenging problem to effectively correlate and retrieve the data of
different media types. Naturally, when correlating an image with textual
description, people focus on not only the alignment between discriminative
image regions and key words, but also the relations lying in the visual and
textual context. Relation understanding is essential for cross-media
correlation learning, which is ignored by prior cross-media retrieval works. To
address the above issue, we propose Cross-media Relation Attention Network
(CRAN) with multi-level alignment. First, we propose visual-language relation
attention model to explore both fine-grained patches and their relations of
different media types. We aim to not only exploit cross-media fine-grained
local information, but also capture the intrinsic relation information, which
can provide complementary hints for correlation learning. Second, we propose
cross-media multi-level alignment to explore global, local and relation
alignments across different media types, which can mutually boost to learn more
precise cross-media correlation. We conduct experiments on 2 cross-media
datasets, and compare with 10 state-of-the-art methods to verify the
effectiveness of proposed approach.
