Unitail: Detecting, Reading, and Matching in Retail Scene
Abstract
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various start-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness. The Unitail and its evaluation server are publicly available at https://unitedretail.github.io/
Cite
Text
Chen et al. "Unitail: Detecting, Reading, and Matching in Retail Scene." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20071-7_41Markdown
[Chen et al. "Unitail: Detecting, Reading, and Matching in Retail Scene." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/chen2022eccv-unitail/) doi:10.1007/978-3-031-20071-7_41BibTeX
@inproceedings{chen2022eccv-unitail,
title = {{Unitail: Detecting, Reading, and Matching in Retail Scene}},
author = {Chen, Fangyi and Zhang, Han and Li, Zaiwang and Dou, Jiachen and Mo, Shentong and Chen, Hao and Zhang, Yongxin and Ahmed, Uzair and Zhu, Chenchen and Savvides, Marios},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-20071-7_41},
url = {https://mlanthology.org/eccv/2022/chen2022eccv-unitail/}
}