5 papers
Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval
Osman Tursun, Simon Denman, Sridha Sridharan +1
Large-scale trademark retrieval is an important content-based image retrieval task. A recent study shows that off-the-shelf deep features aggregated with Regional-Maximum Activatio…
An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval
Osman Tursun, Simon Denman, Sridha Sridharan +2
Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it's real-world applications, and the more realistic…
MTRNet++: One-stage Mask-based Scene Text Eraser
Osman Tursun, Simon Denman, Rui Zeng +3
A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we…
MTRNet: A Generic Scene Text Eraser
Osman Tursun, Rui Zeng, Simon Denman +3
Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is ab…
Component-based Attention for Large-scale Trademark Retrieval
Osman Tursun, Simon Denman, Sabesan Sivapalan +3
The demand for large-scale trademark retrieval (TR) systems has significantly increased to combat the rise in international trademark infringement. Unfortunately, the ranking accur…