Cross-domain Beauty Item Retrieval via Unsupervised Embedding Learning

@article{Lin2019CrossdomainBI,
  title={Cross-domain Beauty Item Retrieval via Unsupervised Embedding Learning},
  author={Zehang Lin and H. Xie and Peipei Kang and Zhenguo Yang and Wenyin Liu and Qing Li},
  journal={Proceedings of the 27th ACM International Conference on Multimedia},
  year={2019}
}
  • Zehang Lin, H. Xie, +3 authors Qing Li
  • Published 2019
  • Computer Science
  • Proceedings of the 27th ACM International Conference on Multimedia
Cross-domain image retrieval is always encountering insufficient labelled data in real world. In this paper, we propose unsupervised embedding learning (UEL) for cross-domain beauty and personal care product retrieval to finetune the convolutional neural network (CNN). More specifically, UEL utilizes the non-parametric softmax to train the CNN model as instance-level classification, which reduces the influence of some inevitable problems (e.g., shape variations). In order to obtain better… Expand
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