A Large-scale Dataset and Benchmark for Similar Trademark Retrieval
This work addresses the challenge of trademark retrieval for legal and business applications by providing a standardized evaluation framework, though it is incremental as it builds on existing methods with new data.
The authors tackled the problem of trademark retrieval by creating a large-scale dataset and benchmark to evaluate existing methods, and they applied deep learning models for the first time in this domain, achieving baseline results but without specific performance numbers.
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.