  In this paper, we propose a novel abstraction-aware sketch-based image
retrieval framework capable of handling sketch abstraction at varied levels.
Prior works had mainly focused on tackling sub-factors such as drawing style
and order, we instead attempt to model abstraction as a whole, and propose
feature-level and retrieval granularity-level designs so that the system builds
into its DNA the necessary means to interpret abstraction. On learning
abstraction-aware features, we for the first-time harness the rich semantic
embedding of pre-trained StyleGAN model, together with a novel
abstraction-level mapper that deciphers the level of abstraction and
dynamically selects appropriate dimensions in the feature matrix
correspondingly, to construct a feature matrix embedding that can be freely
traversed to accommodate different levels of abstraction. For granularity-level
abstraction understanding, we dictate that the retrieval model should not treat
all abstraction-levels equally and introduce a differentiable surrogate Acc.@q
loss to inject that understanding into the system. Different to the
gold-standard triplet loss, our Acc.@q loss uniquely allows a sketch to
narrow/broaden its focus in terms of how stringent the evaluation should be -
the more abstract a sketch, the less stringent (higher q). Extensive
experiments depict our method to outperform existing state-of-the-arts in
standard SBIR tasks along with challenging scenarios like early retrieval,
forensic sketch-photo matching, and style-invariant retrieval.
