  Different from the general visual classification, some classification tasks
are more challenging as they need the professional categories of the images. In
the paper, we call them expert-level classification. Previous fine-grained
vision classification (FGVC) has made many efforts on some of its specific
sub-tasks. However, they are difficult to expand to the general cases which
rely on the comprehensive analysis of part-global correlation and the
hierarchical features interaction. In this paper, we propose Expert Network
(ExpNet) to address the unique challenges of expert-level classification
through a unified network. In ExpNet, we hierarchically decouple the part and
context features and individually process them using a novel attentive
mechanism, called Gaze-Shift. In each stage, Gaze-Shift produces a focal-part
feature for the subsequent abstraction and memorizes a context-related
embedding. Then we fuse the final focal embedding with all memorized
context-related embedding to make the prediction. Such an architecture realizes
the dual-track processing of partial and global information and hierarchical
feature interactions. We conduct the experiments over three representative
expert-level classification tasks: FGVC, disease classification, and artwork
attributes classification. In these experiments, superior performance of our
ExpNet is observed comparing to the state-of-the-arts in a wide range of
fields, indicating the effectiveness and generalization of our ExpNet. The code
will be made publicly available.
