  Convolutional Neural Networks (CNNs) have revolutionized the understanding of
visual content. This is mainly due to their ability to break down an image into
smaller pieces, extract multi-scale localized features and compose them to
construct highly expressive representations for decision making. However, the
convolution operation is unable to capture long-range dependencies such as
arbitrary relations between pixels since it operates on a fixed-size window.
Therefore, it may not be suitable for discriminating subtle changes (e.g.
fine-grained visual recognition). To this end, our proposed method captures the
high-level long-range dependencies by exploring Graph Convolutional Networks
(GCNs), which aggregate information by establishing relationships among
multi-scale hierarchical regions. These regions consist of smaller (closer
look) to larger (far look), and the dependency between regions is modeled by an
innovative attention-driven message propagation, guided by the graph structure
to emphasize the neighborhoods of a given region. Our approach is simple yet
extremely effective in solving both the fine-grained and generic visual
classification problems. It outperforms the state-of-the-arts with a
significant margin on three and is very competitive on other two datasets.
