Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval
Kaiyue Pang, Yi-zhe Song, Tony Xiang and Timothy Hospedales
Abstract
The key challenge for learning a fine-grained sketch-based image retrieval (FG-SBIR)
model is to bridge the domain gap between photo and sketch. Existing models learn a
deep joint embedding space with discriminative losses where a photo and a sketch can
be compared. In this paper, we propose a novel discriminative-generative hybrid model
by introducing a generative task of cross-domain image synthesis. This task enforces the
learned embedding space to preserve all the domain invariant information that is useful
for cross-domain reconstruction, thus explicitly reducing the domain gap as opposed to
existing models.
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DOI
10.5244/C.31.46
https://dx.doi.org/10.5244/C.31.46
Citation
Kaiyue Pang, Yi-zhe Song, Tony Xiang and Timothy Hospedales. Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval. In T.K. Kim, S. Zafeiriou, G. Brostow and K. Mikolajczyk, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 46.1-46.12. BMVA Press, September 2017.
Bibtex
@inproceedings{BMVC2017_46,
title={Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval},
author={Kaiyue Pang, Yi-zhe Song, Tony Xiang and Timothy Hospedales},
year={2017},
month={September},
pages={46.1-46.12},
articleno={46},
numpages={12},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
doi={10.5244/C.31.46},
isbn={1-901725-60-X},
url={https://dx.doi.org/10.5244/C.31.46}
}