# Scalable Logo Recognition using Proxies

arXiv: https://arxiv.org/abs/1811.08009 · HTML: https://mlsanity.com/paper/1811.08009/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network](https://mlsanity.com/paper/2108.04644/) (2021)
- [Deep Learning for Logo Detection: A Survey](https://mlsanity.com/paper/2210.04399/) (2022)
- [RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition](https://mlsanity.com/paper/2312.16792/) (2023)
- [Image-Text Pre-Training for Logo Recognition](https://mlsanity.com/paper/2309.10206/) (2023)
- [Makeup216: Logo Recognition with Adversarial Attention Representations](https://mlsanity.com/paper/2112.06533/) (2021)
- [Catalog Phrase Grounding (CPG): Grounding of Product Textual Attributes in Product Images for e-commerce Vision-Language Applications](https://mlsanity.com/paper/2308.16354/) (2023)
- [Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification](https://mlsanity.com/paper/2211.12926/) (2022)

## Abstract (arXiv)

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.