One-Shot Learning from Prototype Stock Keeping Unit Images
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Published:2024-08-28
Issue:9
Volume:15
Page:526
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ISSN:2078-2489
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Container-title:Information
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language:en
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Short-container-title:Information
Author:
Kowalczyk Aleksandra1ORCID, Sarwas Grzegorz12ORCID
Affiliation:
1. Faculty of Electrical Engineering, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland 2. Omniaz Sp. z o.o., ul. Narutowicza 40/1, 90-135 Łódź, Poland
Abstract
This paper highlights the importance of one-shot learning from prototype Stock Keeping Unit (SKU) images for efficient product recognition in retail and inventory management. Traditional methods require large supervised datasets to train deep neural networks, which can be costly and impractical. One-shot learning techniques mitigate this issue by enabling classification from a single prototype image per product class, thus reducing data annotation efforts. We introduce the Variational Prototyping Encoder (VPE), a novel deep neural network for one-shot classification. Utilizing a support set of prototype SKU images, VPE learns to classify query images by capturing image similarity and prototypical concepts. Unlike metric learning-based approaches, VPE pre-learns image translation from real-world object images to prototype images as a meta-task, facilitating efficient one-shot classification with minimal supervision. Our research demonstrates that VPE effectively reduces the need for extensive datasets by utilizing a single image per class while accurately classifying query images into their respective categories, thus providing a practical solution for product classification tasks.
Reference30 articles.
1. Merler, M., Galleguillos, C., and Belongie, S. (2007, January 17–22). Recognizing Groceries in situ Using in vitro Training Data. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA. 2. George, M., Mircic, D., Sörös, G., Floerkemeier, C., and Mattern, F. (2015, January 7–13). Fine-Grained Product Class Recognition for Assisted Shopping. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile. 3. Melek, C.G., Sonmez, E.B., and Albayrak, S. (2017, January 5–8). A survey of product recognition in shelf images. Proceedings of the 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey. 4. Tonioni, A., Serra, E., and Di Stefano, L. (2018, January 12–14). A deep learning pipeline for product recognition on store shelves. Proceedings of the 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France. 5. Geng, W., Han, F., Lin, J., Zhu, L., Bai, J., Wang, S., He, L., Xiao, Q., and Lai, Z. (2018, January 22–26). Fine-Grained Grocery Product Recognition by One-Shot Learning. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea. MM’18.
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