Abstract. Due to the succinct nature of free-hand sketch drawings,
sketch-based image retrieval (SBIR) has abundant practical use cases
in consumer electronics. However, SBIR remains a long-standing unsolved problem mainly because of the significant discrepancy between
the sketch domain and the image domain. In this work, we propose a
Generative Domain-migration Hashing (GDH) approach, which for the
first time generates hashing codes from synthetic natural images that are
migrated from sketches. The generative model learns a mapping that the
distributions of sketches can be indistinguishable from the distribution
of natural images using an adversarial loss, and simultaneously learns
an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability. With the robust mapping learned from the
generative model, GDH can migrate sketches to their indistinguishable
image counterparts while preserving the domain-invariant information of
sketches. With an end-to-end multi-task learning framework, the generative model and binarized hashing codes can be jointly optimized. Comprehensive experiments of both category-level and fine-grained SBIR on
multiple large-scale datasets demonstrate the consistently balanced superiority of GDH in terms of efficiency, memory costs and effectiveness