  Sketch as an image search query is an ideal alternative to text in capturing
the fine-grained visual details. Prior successes on fine-grained sketch-based
image retrieval (FG-SBIR) have demonstrated the importance of tackling the
unique traits of sketches as opposed to photos, e.g., temporal vs. static,
strokes vs. pixels, and abstract vs. pixel-perfect. In this paper, we study a
further trait of sketches that has been overlooked to date, that is, they are
hierarchical in terms of the levels of detail -- a person typically sketches up
to various extents of detail to depict an object. This hierarchical structure
is often visually distinct. In this paper, we design a novel network that is
capable of cultivating sketch-specific hierarchies and exploiting them to match
sketch with photo at corresponding hierarchical levels. In particular, features
from a sketch and a photo are enriched using cross-modal co-attention, coupled
with hierarchical node fusion at every level to form a better embedding space
to conduct retrieval. Experiments on common benchmarks show our method to
outperform state-of-the-arts by a significant margin.
