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Multi-attention based cross-domain beauty product image retrieval

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References

  1. 1

    Cheng W-H, Jia J, Huang J. Half Million Beauty Product Image Recognition. 2018. https://challenge2018.perfectcorp.com/

  2. 2

    Lin Z, Yang Z, Huang F, et al. Regional maximum activations of convolutions with attention for cross-domain beauty and personal care product retrieval. In: Proceedings of ACM Conference on Multimedia, 2018. 2073–2077

  3. 3

    Wang Q, Lai J X, Xu K, et al. Beauty product image retrieval based on multi-feature fusion and feature aggregation. In: Proceedings of ACM Conference on Multimedia, 2018. 2063–2067

  4. 4

    Lim J H, Japar N, Ng C C, et al. Unprecedented usage of pre-trained CNNs on beauty product. In: Proceedings of ACM Conference on Multimedia, 2018. 2068–2072

  5. 5

    Sun H Q, Pang Y W. GlanceNets-efficient convolutional neural networks with adaptive hard example mining. Sci China Inf Sci, 2018, 61: 109101

  6. 6

    Zhong J, Sun Y X, Yu Y L, et al. Attribute-guided network for cross-modal zero-shot hashing. IEEE Trans Neural Netw Learn Syst, 2018. doi: https://doi.org/10.1109/TNNLS.2019.2904991

  7. 7

    Li H J, Wang X H, Tang J H, et al. Combining global and local matching of multiple features for precise item image retrieval. Multimedia Syst, 2013, 19: 37–49

  8. 8

    Zhou X, Yao C, Wen H, et al. East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 5551–5560

  9. 9

    Tolias G, Sicre R, Jegou H. Particular object retrieval with integral max-pooling of CNN activations. In: Proceedings of the 4th International Conference on Learning Representations, San Juan, 2016

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61772108, 61932020, 61976038).

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Correspondence to Haojie Li.

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Experiments. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Wang, Z., Liu, X., Lin, J. et al. Multi-attention based cross-domain beauty product image retrieval. Sci. China Inf. Sci. 63, 120112 (2020). https://doi.org/10.1007/s11432-019-2721-0

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