  Humans can naturally understand an image in depth with the aid of rich
knowledge accumulated from daily lives or professions. For example, to achieve
fine-grained image recognition (e.g., categorizing hundreds of subordinate
categories of birds) usually requires a comprehensive visual concept
organization including category labels and part-level attributes. In this work,
we investigate how to unify rich professional knowledge with deep neural
network architectures and propose a Knowledge-Embedded Representation Learning
(KERL) framework for handling the problem of fine-grained image recognition.
Specifically, we organize the rich visual concepts in the form of knowledge
graph and employ a Gated Graph Neural Network to propagate node message through
the graph for generating the knowledge representation. By introducing a novel
gated mechanism, our KERL framework incorporates this knowledge representation
into the discriminative image feature learning, i.e., implicitly associating
the specific attributes with the feature maps. Compared with existing methods
of fine-grained image classification, our KERL framework has several appealing
properties: i) The embedded high-level knowledge enhances the feature
representation, thus facilitating distinguishing the subtle differences among
subordinate categories. ii) Our framework can learn feature maps with a
meaningful configuration that the highlighted regions finely accord with the
nodes (specific attributes) of the knowledge graph. Extensive experiments on
the widely used Caltech-UCSD bird dataset demonstrate the superiority of our
KERL framework over existing state-of-the-art methods.
