  In this paper, we tackle for the first time, the problem of self-supervised
representation learning for free-hand sketches. This importantly addresses a
common problem faced by the sketch community -- that annotated supervisory data
are difficult to obtain. This problem is very challenging in that sketches are
highly abstract and subject to different drawing styles, making existing
solutions tailored for photos unsuitable. Key for the success of our
self-supervised learning paradigm lies with our sketch-specific designs: (i) we
propose a set of pretext tasks specifically designed for sketches that mimic
different drawing styles, and (ii) we further exploit the use of a textual
convolution network (TCN) in a dual-branch architecture for sketch feature
learning, as means to accommodate the sequential stroke nature of sketches. We
demonstrate the superiority of our sketch-specific designs through two
sketch-related applications (retrieval and recognition) on a million-scale
sketch dataset, and show that the proposed approach outperforms the
state-of-the-art unsupervised representation learning methods, and
significantly narrows the performance gap between with supervised
representation learning.
