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.

本文通过优化基于细粒度素描的图像检索方法，使用增强学习及跨模态检索框架实现检索效率的提升与最少笔画查询目标图像的目标，是一种有效的搜索框架。