# A Large-scale Dataset and Benchmark for Similar Trademark Retrieval

arXiv: https://arxiv.org/abs/1701.05766 · HTML: https://mlsanity.com/paper/1701.05766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2017.

## Built on by (most cited first, 4 of 4)

- [Privacy-sensitive Objects Pixelation for Live Video Streaming](https://mlsanity.com/paper/2101.00604/) (2021)
- [Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features](https://mlsanity.com/paper/2205.05419/) (2022)
- [Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval](https://mlsanity.com/paper/2104.07240/) (2021)
- [Segment Augmentation and Differentiable Ranking for Logo Retrieval](https://mlsanity.com/paper/2209.02482/) (2022)

## Abstract (arXiv)

Trademark retrieval (TR) has become an important yet challenging problem due to an ever increasing trend in trademark applications and infringement incidents. There have been many promising attempts for the TR problem, which, however, fell impracticable since they were evaluated with limited and mostly trivial datasets. In this paper, we provide a large-scale dataset with benchmark queries with which different TR approaches can be evaluated systematically. Moreover, we provide a baseline on this benchmark using the widely-used methods applied to TR in the literature. Furthermore, we identify and correct two important issues in TR approaches that were not addressed before: reversal of contrast, and presence of irrelevant text in trademarks severely affect the TR methods. Lastly, we applied deep learning, namely, several popular Convolutional Neural Network models, to the TR problem. To the best of the authors, this is the first attempt to do so.