  Cross-media retrieval is to return the results of various media types
corresponding to the query of any media type. Existing researches generally
focus on coarse-grained cross-media retrieval. When users submit an image of
"Slaty-backed Gull" as a query, coarse-grained cross-media retrieval treats it
as "Bird", so that users can only get the results of "Bird", which may include
other bird species with similar appearance (image and video), descriptions
(text) or sounds (audio), such as "Herring Gull". Such coarse-grained
cross-media retrieval is not consistent with human lifestyle, where we
generally have the fine-grained requirement of returning the exactly relevant
results of "Slaty-backed Gull" instead of "Herring Gull". However, few
researches focus on fine-grained cross-media retrieval, which is a highly
challenging and practical task. Therefore, in this paper, we first construct a
new benchmark for fine-grained cross-media retrieval, which consists of 200
fine-grained subcategories of the "Bird", and contains 4 media types, including
image, text, video and audio. To the best of our knowledge, it is the first
benchmark with 4 media types for fine-grained cross-media retrieval. Then, we
propose a uniform deep model, namely FGCrossNet, which simultaneously learns 4
types of media without discriminative treatments. We jointly consider three
constraints for better common representation learning: classification
constraint ensures the learning of discriminative features, center constraint
ensures the compactness characteristic of the features of the same subcategory,
and ranking constraint ensures the sparsity characteristic of the features of
different subcategories. Extensive experiments verify the usefulness of the new
benchmark and the effectiveness of our FGCrossNet. They will be made available
at https://github.com/PKU-ICST-MIPL/FGCrossNet_ACMMM2019.
