NLP Cross-Domain Recognition of Retail Products
2022 (English)In: ICMLT '22: Proceedings of the 2022 7th International Conference on Machine Learning Technologies, March 2022, Association for Computing Machinery (ACM), 2022, p. 237-243Conference paper, Published paper (Refereed)
Abstract [en]
Self‐checkout systems aim to provide a seamless and high-quality shopping experience and increase the profitability of stores. These advantages come with some challenges such as shrinkage loss. To overcome these challenges, automatic recognition of the purchased products is a potential solution. In this context, one of the big issues that emerge is the data shifting, which is caused by the difference between the environment in which the recognition model is trained and the environment in which the model is deployed. In this paper, we use transfer learning to handle the shift caused by the change of camera and lens or their position as well as critical factors, mainly lighting, reflection, and occlusion. We motivate the use of Natural Language Processing (NLP) techniques on textual data extracted from images instead of using image recognition to study the efficiency of transfer learning techniques. The results show that cross-domain NLP retail recognition using the BERT language model only results in a small reduction in performance between the source and target domain. Furthermore, a small number of additional training samples from the target domain improves the model to perform comparable as a model trained on the source domain.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022. p. 237-243
Keywords [en]
Image recognition, Learning systems, Natural language processing systems, Sales, Text processing, Transfer learning, BERT, Cross-domain, Domain adaptation, Language processing, Natural language processing, Natural languages, Product recognition, Retail, Text classification, Classification (of information), NLP
National Category
Computer Sciences
Research subject
Virtual Production Development (VPD)
Identifiers
URN: urn:nbn:se:his:diva-22974DOI: 10.1145/3529399.3529436ISI: 001053939400037Scopus ID: 2-s2.0-85132414844ISBN: 978-1-4503-9574-8 (print)OAI: oai:DiVA.org:his-22974DiVA, id: diva2:1780233
Conference
ICMLT 2022: 7th International Conference on Machine Learning Technologies (ICMLT), Virtual Conference, 11-13 March 2022
Funder
Knowledge Foundation, DATAKIND 20190194
Note
This work was supported by the Swedish Knowledge Foundation (DATAKIND 20190194), the company ITAB, and Smart Industry Sweden (KKS-2020-0044).
© 2022 Association for Computing Machinery.
2023-07-052023-07-052024-07-08Bibliographically approved