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Domain-aware double attention network for zero-shot sketch-based image retrieval with similarity loss

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Abstract

Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted increasing attention in computer vision, with the aim of searching the natural images that match a given sketch under the setting of zero-shot learning. In this paper, we propose a novel ZS-SBIR method based on the specifically developed domain-aware dual attention (DADA) module and similarity loss, which utilizes prior knowledge more fully than previous work. The DADA module emphasizes the importance of different channels and spaces according to the prior knowledge of whether the input is a sketch or a natural image. To preserve more prior knowledge from the models pre-trained on ImageNet, a similarity loss function is designed to supervise inter-class similarity. Furthermore, the center loss is introduced into our model, which can bridge the representation gap between the sketch domain and the natural image domain. We perform experiments on the TU-Berlin extended dataset and the Sketchy extended dataset, and the corresponding results verify the effectiveness of the proposed method.

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Data availability

We declare that we have no financial or personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service or company that could be construed as influencing the position presented in, or the review of the manuscript entitled. The data that support the findings of this study are openly available.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (62202003), Key Scientific Research Project of in Anhui Province (2022AH050093), Science and Technology Major Project of Anhui Province (202203a05020027) and Major University Science Research Project of AnHui Province (KJ2021ZD0004).

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Authors and Affiliations

  1. School of Integrated Circuits, Anhui University, Hefei, 230601, China

    Ming Zhu, Chen Zhao, Feiyang Gu, Yu Liu & Xin Li

  2. National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, 230601, China

    Nian Wang

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  1. Ming Zhu
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  2. Chen Zhao
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  3. Nian Wang
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  4. Feiyang Gu
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  5. Yu Liu
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Correspondence to Yu Liu.

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Zhu, M., Zhao, C., Wang, N. et al. Domain-aware double attention network for zero-shot sketch-based image retrieval with similarity loss. Vis Comput 40, 3091–3101 (2024). https://doi.org/10.1007/s00371-023-03012-8

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