  Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of
retrieving a particular photo instance given a user's query sketch. Its
widespread applicability is however hindered by the fact that drawing a sketch
takes time, and most people struggle to draw a complete and faithful sketch. In
this paper, we reformulate the conventional FG-SBIR framework to tackle these
challenges, with the ultimate goal of retrieving the target photo with the
least number of strokes possible. We further propose an on-the-fly design that
starts retrieving as soon as the user starts drawing. To accomplish this, we
devise a reinforcement learning-based cross-modal retrieval framework that
directly optimizes rank of the ground-truth photo over a complete sketch
drawing episode. Additionally, we introduce a novel reward scheme that
circumvents the problems related to irrelevant sketch strokes, and thus
provides us with a more consistent rank list during the retrieval. We achieve
superior early-retrieval efficiency over state-of-the-art methods and
alternative baselines on two publicly available fine-grained sketch retrieval
datasets.
