| Metu dataset: A big dataset for benchmarking trademark retrieval O Tursun, S Kalkan 2015 14th IAPR International Conference on Machine Vision Applications (MVA …, 2015 | 23 | 2015 |
| A large-scale dataset and benchmark for similar trademark retrieval O Tursun, C Aker, S Kalkan arXiv preprint arXiv:1701.05766, 2017 | 21 | 2017 |
| Mtrnet: A generic scene text eraser O Tursun, R Zeng, S Denman, S Sivapalan, S Sridharan, C Fookes 2019 International Conference on Document Analysis and Recognition (ICDAR …, 2019 | 20 | 2019 |
| Noisy Uyghur text normalization O Tursun, R Cakıcı Proceedings of the 3rd Workshop on Noisy User-generated Text, 85-93, 2017 | 16 | 2017 |
| Component-based attention for large-scale trademark retrieval O Tursun, S Denman, S Sivapalan, S Sridharan, C Fookes, S Mau IEEE Transactions on Information Forensics and Security, 2019 | 13 | 2019 |
| MTRNet++: One-stage mask-based scene text eraser O Tursun, S Denman, R Zeng, S Sivapalan, S Sridharan, C Fookes Computer Vision and Image Understanding 201, 103066, 2020 | 9 | 2020 |
| Analyzing deep features for trademark retrieval C Aker, O Tursun, S Kalkan 2017 25th Signal Processing and Communications Applications Conference (SIU …, 2017 | 8 | 2017 |
| An efficient framework for zero-shot sketch-based image retrieval O Tursun, S Denman, S Sridharan, E Goan, C Fookes Pattern Recognition, 108528, 2022 | 6 | 2022 |
| Learning Regional Attention Over Multi-Resolution Deep Convolutional Features For Trademark Retrieval O Tursun, S Denman, S Sridharan, C Fookes 2021 IEEE International Conference on Image Processing (ICIP), 2393-2397, 2021 | 2 | 2021 |
| Learning Test-time Augmentation for Content-based Image Retrieval O Tursun, S Denman, S Sridharan, C Fookes arXiv preprint arXiv:2002.01642, 2020 | 2* | 2020 |
| Missing ingredients in optimising large-scale image retrieval with deep features O Tursun Queensland University of Technology, 2022 | | 2022 |
Simon DenmanQueensland University of TechnologyVerified email at qut.edu.au
