Joint Training of Product Detection and Recognition Using Task-Specific Datasets

Floris De Feyter, Toon Goedemé

2023

Abstract

Training a single model jointly for detection and recognition is typically done with a dataset that is fully annotated, i.e., the annotations consist of boxes with class labels. In the case of retail product detection and recognition, however, developing such a dataset is very expensive due to the large variety of products. It would be much more cost-efficient and scalable if we could employ two task-specific datasets: one detection-only and one recognition-only dataset. Unfortunately, experiments indicate a significant drop in performance when trained on task-specific data. Due to the potential cost savings, we are convinced that more research should be done on this matter and, therefore, we propose a set of training procedures that allows us to carefully investigate the differences between training with fully-annotated vs. task-specific data. We demonstrate this on a product detection and recognition dataset and as such reveal one of the core issues that is inherent to task-specific training. We hope that our results will motivate and inspire researchers to further look into the problem of employing task-specific datasets to train joint detection and recognition models.

Paper Citation

in Harvard Style

De Feyter F. and Goedemé T. (2023). Joint Training of Product Detection and Recognition Using Task-Specific Datasets. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 715-722. DOI: 10.5220/0011725100003417

in Bibtex Style

@conference{visapp23,
author={Floris De Feyter and Toon Goedemé},
title={Joint Training of Product Detection and Recognition Using Task-Specific Datasets},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={715-722},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011725100003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Joint Training of Product Detection and Recognition Using Task-Specific Datasets
SN - 978-989-758-634-7
AU - De Feyter F.
AU - Goedemé T.
PY - 2023
SP - 715
EP - 722
DO - 10.5220/0011725100003417
PB - SciTePress