Abstract
Product image matching is essential on e-commerce platforms since the target customers have to distinguish the same products organized in varied manners by different companies. Machine learning methods can be applied for such kind of task, which treat it as a classification problem. However, it is intrinsically intractable to enforce the model to expand to new products due to its invisibility to unknown categories. Metric learning has emerged as a promising approach to address this issue, which is designed to measure the distances between data points. In this paper, a metric learning based vision transformer (ML-VIT) is proposed to learn the embeddings of the product image data and match the images with small Euclidean distances to the same products. The proposed ML-VIT adopts Arcface loss to achieve intra-class compactness and inter-class dispersion. Compared with Siamese neural network and other pre-trained models in terms of F1 score and accuracy, ML-VIT is proved to yield modest embeddings for product image matching.
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Acknowledgements
The work described in this paper was substantially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region [CityU 11200218], one grant from the Health and Medical Research Fund, the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region [07181426], and the funding from Hong Kong Institute for Data Science (HKIDS) at City University of Hong Kong. The work described in this paper was partially supported by two grants from City University of Hong Kong (CityU 11202219, CityU 11203520). This research is also supported by the National Natural Science Foundation of China under Grant No. 32000464.
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Huang, L., Shao, W., Wang, F., Xie, W., Wong, KC. (2021). Metric Learning Based Vision Transformer for Product Matching. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_1
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