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.

Illustration of proposed on-the-fly framework's efficacy over a baseline FG-SBIR method trained with completed sketches only. For this particular example, our method needs only 30% of the complete sketch to include the true match in the top-10 rank list, compared to 80% for the baseline. Top-5 photo images retrieved by either framework are shown here, in progressive sketch-rendering steps of 10%. The number at the bottom denotes the paired (true match) photo's rank at every stage.

@inproceedings{bhunia2020sketch,
  title={Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval},
  author={Ayan Kumar Bhunia and Yongxin Yang and Timothy M. Hospedales and Tao Xiang and Yi-Zhe Song},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}