Scalable Logo Recognition Using Proxies
Abstract
Logo recognition is the task of identifying and classifying logos. Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations of logos, brands and re-training to cover every variation is impractical. In this paper, we formulate logo recognition as a few-shot object detection problem. The two main components in our pipeline are universal logo detector and few-shot logo recognizer. The universal logo detector is a class-agnostic deep object detector network which tries to learn the characteristics of what makes a logo. It predicts bounding boxes on likely logo regions. These logo regions are then classified by logo recognizer using nearest neighbor search, trained by triplet loss using proxies. We also annotated a first of its kind product logo dataset containing 2000 logos from 295K images collected from Amazon called PL2K. Our pipeline achieves 97% recall with 0.6 mAP on PL2K test dataset and state-of-the-art 0.565 mAP on the publicly available FlickrLogos-32 test set without fine-tuning.
Cite
Text
Fehérvári and Appalaraju. "Scalable Logo Recognition Using Proxies." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00081Markdown
[Fehérvári and Appalaraju. "Scalable Logo Recognition Using Proxies." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/fehervari2019wacv-scalable/) doi:10.1109/WACV.2019.00081BibTeX
@inproceedings{fehervari2019wacv-scalable,
title = {{Scalable Logo Recognition Using Proxies}},
author = {Fehérvári, István and Appalaraju, Srikar},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2019},
pages = {715-725},
doi = {10.1109/WACV.2019.00081},
url = {https://mlanthology.org/wacv/2019/fehervari2019wacv-scalable/}
}