Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song, “Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020.
https://arxiv.org/abs/2002.10310
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.
Figure: (a) A conventional FG-SBIR framework trained using triplet loss. (b) Our proposed reinforcement learning based framework that takes into account a complete sketch rendering episode. Key locks signifies particular weights are fixed during RL training.
Figure: 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.
~A basic Policy-Gradient (not PPO which is our final version) based sketch branch finetuning (via Reinforcement Learning) code is uploaded at this time to help any reader understand how our framework works.
Feature extracted from baseline triplet loss based network: Download
If you find this article useful in your research, please consider citing:
@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}
}