SCIENCE CHINA Information Sciences, Volume 63, Issue 2: 120112(2020) https://doi.org/10.1007/s11432-019-2721-0

Multi-attention based cross-domain beauty product image retrieval

More info
  • ReceivedJul 28, 2019
  • AcceptedNov 12, 2019
  • PublishedJan 14, 2020


There is no abstract available for this article.


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




[1] Wen-Huang Cheng, Jia Jia and Huang, J. Perfect corp. challenge 2018: Half million beauty product image recognition. 2018. https://challenge2018.perfectcorp.com/. Google Scholar

[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 Multimedia Conference on Multimedia Conference, 2018. 2073--2077. Google Scholar

[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 Multimedia Conference on Multimedia Conference, 2018. 2063--2067. Google Scholar

[4] Lim J H, Japar N, Ng C C, et al. Unprecedented usage of pre-trained cnns on beauty product. In: Proceedings of ACM Multimedia Conference on Multimedia Conference, 2018. 2068--2072. Google Scholar

[5] Sun H, Pang Y. GlanceNets - efficient convolutional neural networks with adaptive hard example mining. Sci China Inf Sci, 2018, 61: 109101 CrossRef Google Scholar

[6] Zhong J, Sun Y X, Yu Y L, et al. Attribute-guided network for cross-modal zero-shot hashing. IEEE Transactions on Neural Networks and Learning Systems, 2018. DOI: 10.1109/TNNLS.2019.2904991. Google Scholar

[7] Li H, Wang X, Tang J. Combining global and local matching of multiple features for precise item image retrieval. Multimedia Syst, 2013, 19: 37-49 CrossRef Google Scholar

[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. Google Scholar

[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. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有