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Fine-Grained Product Classification on Leaflet Advertisements

  • In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task,In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach "Classification by Text" uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at https://github.com/ladwigd/Leaflet-Product-Classification.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8241
Bibliografische Angaben
Title (English):Fine-Grained Product Classification on Leaflet Advertisements
Conference:Workshop on Fine-Grained Visual Categorization (10. : June 18th @ CVPR 2023 : Vancouver)
Author:Daniel LadwigStaff Member, Bianca LammStaff Member, Janis KeuperStaff MemberORCiDGND
Year of Publication:2023
First Page:1
Last Page:5
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Tag:Deep Leaning
Formale Angaben
Relevance:Konferenzbeitrag: h5-Index < 30
Open Access: Open Access 
 Diamond 
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
ArXiv Id:http://arxiv.org/abs/2305.03706