Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Multi-attention based cross-domain beauty product image retrieval

  • Letter
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

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

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

    Article  Google Scholar 

  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. 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

    Article  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

  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

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haojie Li.

Additional information

Supporting information

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.

Supplementary File

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-019-2721-0