  The performance of a zero-shot sketch-based image retrieval (ZS-SBIR) task is
primarily affected by two challenges. The substantial domain gap between image
and sketch features needs to be bridged, while at the same time the side
information has to be chosen tactfully. Existing literature has shown that
varying the semantic side information greatly affects the performance of
ZS-SBIR. To this end, we propose a novel graph transformer based zero-shot
sketch-based image retrieval (GTZSR) framework for solving ZS-SBIR tasks which
uses a novel graph transformer to preserve the topology of the classes in the
semantic space and propagates the context-graph of the classes within the
embedding features of the visual space. To bridge the domain gap between the
visual features, we propose minimizing the Wasserstein distance between images
and sketches in a learned domain-shared space. We also propose a novel
compatibility loss that further aligns the two visual domains by bridging the
domain gap of one class with respect to the domain gap of all other classes in
the training set. Experimental results obtained on the extended Sketchy,
TU-Berlin, and QuickDraw datasets exhibit sharp improvements over the existing
state-of-the-art methods in both ZS-SBIR and generalized ZS-SBIR.
