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 fewshot logo recognizer. The universal logo detector is a classagnostic 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.

@article{istvan2019,
  author    = {Istv{\'{a}}n Feh{\'{e}}rv{\'{a}}ri and Srikar Appalaraju},
  title     = {Scalable Logo Recognition using Proxies},
  journal   = {IEEE WACV},
  year      = {2019},
  url       = {http://arxiv.org/abs/1811.08009},
  timestamp = {Mon, 26 Nov 2018 12:52:45 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1811-08009},
  bibsource = {dblp computer science bibliography, https://dblp.org}
} 

See paper for Quantitative results