51 citations · 148 across the 10 of their papers we have counts for
11 papers
PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval
Osman Tursun, Sinan Kalkan, Simon Denman +1
Zero-shot Composed Image Retrieval (ZS-CIR) enables image search using a reference image and a text prompt without requiring specialized text-image composition networks trained on…
Part-based Quantitative Analysis for Heatmaps
Osman Tursun, Sinan Kalkan, Simon Denman +2
Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhanc…
Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Models
Osman Tursun, Simon Denman, Sridha Sridharan +1
Heatmaps are widely used to interpret deep neural networks, particularly for computer vision tasks, and the heatmap-based explainable AI (XAI) techniques are a well-researched topi…
SESS: Saliency Enhancing with Scaling and Sliding
Osman Tursun, Simon Denman, Sridha Sridharan +1
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techni…
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…
Learning Test-time Augmentation for Content-based Image Retrieval
Osman Tursun, Simon Denman, Sridha Sridharan +1
Off-the-shelf convolutional neural network features achieve outstanding results in many image retrieval tasks. However, their invariance to target data is pre-defined by the networ…
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…
A Large-scale Dataset and Benchmark for Similar Trademark Retrieval
Osman Tursun, Cemal Aker, Sinan Kalkan
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…