  Fine-grained visual parsing, including fine-grained part segmentation and
fine-grained object recognition, has attracted considerable critical attention
due to its importance in many real-world applications, e.g., agriculture,
remote sensing, and space technologies. Predominant research efforts tackle
these fine-grained sub-tasks following different paradigms, while the inherent
relations between these tasks are neglected. Moreover, given most of the
research remains fragmented, we conduct an in-depth study of the advanced work
from a new perspective of learning the part relationship. In this perspective,
we first consolidate recent research and benchmark syntheses with new
taxonomies. Based on this consolidation, we revisit the universal challenges in
fine-grained part segmentation and recognition tasks and propose new solutions
by part relationship learning for these important challenges. Furthermore, we
conclude several promising lines of research in fine-grained visual parsing for
future research.
