Abstract
To see is to sketch – free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantly. Furthermore, human sketches exhibit various levels of sophistication and abstraction even when depicting the same object instance in a reference photo. This means that even if photo-sketch pairs are available, they only provide weak supervision signal to learn a translation model. Compared with existing supervised approaches that solve the problem of D(E(photo)) → sketch), where E(·) and D(·) denote encoder and decoder respectively, we take advantage of the inverse problem (e.g., D(E(sketch) → photo), and combine with the unsupervised learning tasks of within-domain reconstruction, all within a multi-task learning framework. Compared with existing unsupervised approaches based on cycle consistency (i.e., D(E(D(E(photo)))) → photo), we introduce a shortcut consistency enforced at the encoder bottleneck (e.g., D(E(photo)) → photo) to exploit the additional self-supervision. Both qualitative and quantitative results show that the proposed model is superior to a number of state-of-the-art alternatives. We also show that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively alleviating the problem of sketch data scarcity.
Original language | English |
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Title of host publication | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 801-810 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-5386-6420-9 |
DOIs | |
Publication status | Published - 17 Dec 2018 |
Event | Computer Vision and Pattern Recognition 2018 - Salt Lake City, United States Duration: 18 Jun 2018 → 22 Jun 2018 http://cvpr2018.thecvf.com/ http://cvpr2018.thecvf.com/ http://cvpr2018.thecvf.com/ |
Publication series
Name | |
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ISSN (Electronic) | 2575-7075 |
Conference
Conference | Computer Vision and Pattern Recognition 2018 |
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Abbreviated title | CVPR 2018 |
Country | United States |
City | Salt Lake City |
Period | 18/06/18 → 22/06/18 |
Internet address |