# Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification

arXiv: https://arxiv.org/abs/2211.12926 · HTML: https://mlsanity.com/paper/2211.12926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis](https://mlsanity.com/paper/1904.01906/) (2019)
- [LogoDet-3K: A Large-Scale Image Dataset for Logo Detection](https://mlsanity.com/paper/2008.05359/) (2020)
- [Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images](https://mlsanity.com/paper/1904.08482/) (2019)
- [Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification](https://mlsanity.com/paper/1911.07924/) (2019)
- [Deep Learning Logo Detection with Data Expansion by Synthesising Context](https://mlsanity.com/paper/1612.09322/) (2016)
- [Open Set Logo Detection and Retrieval](https://mlsanity.com/paper/1710.10891/) (2017)
- [Open Logo Detection Challenge](https://mlsanity.com/paper/1807.01964/) (2018)
- [LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks](https://mlsanity.com/paper/1511.02462/) (2015)
- [DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer](https://mlsanity.com/paper/1510.02131/) (2015)
- [Deep Learning for Logo Recognition](https://mlsanity.com/paper/1701.02620/) (2017)
- [Scalable Logo Recognition using Proxies](https://mlsanity.com/paper/1811.08009/) (2018)
- [Large Scale Open-Set Deep Logo Detection](https://mlsanity.com/paper/1911.07440/) (2019)
- [A Deep One-Shot Network for Query-based Logo Retrieval](https://mlsanity.com/paper/1811.01395/) (2018)
- [Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition](https://mlsanity.com/paper/1712.01907/) (2017)

## Abstract (arXiv)

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging than traditionally-studied 'closed-set' and 'large-scale training samples per category' logo recognition settings. We propose a novel multi-view textual-visual encoding framework that encodes text appearing in the logos as well as the graphical design of the logos to learn robust contrastive representations. These representations are jointly learned for multiple views of logos over a batch and thereby they generalize well to unseen logos. We evaluate our proposed framework for cropped logo verification, cropped logo identification, and end-to-end logo identification in natural scene tasks; and compare it against state-of-the-art methods. Further, the literature lacks a 'very-large-scale' collection of reference logo images that can facilitate the study of one-hundred thousand-scale logo identification. To fill this gap in the literature, we introduce Wikidata Reference Logo Dataset (WiRLD), containing logos for 100K business brands harvested from Wikidata. Our proposed framework that achieves an area under the ROC curve of 91.3% on the QMUL-OpenLogo dataset for the verification task, outperforms state-of-the-art methods by 9.1% and 2.6% on the one-shot logo identification task on the Toplogos-10 and the FlickrLogos32 datasets, respectively. Further, we show that our method is more stable compared to other baselines even when the number of candidate logos is on a 100K scale.