Computer vision aims at extracting useful information (e.g., objects, their boundaries, motion and 3D information, etc.) from images and videos. In this project, we aim at improving the state-of-the-art on various computer vision applications utilizing machine learning and optimization techniques.


Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder.
Zhiwei Xu, Thalaiyasingam Ajanthan, and Richard Hartley.
arxiv:2010.04363, November 2020.
[pdf] [arxiv] [bib]

Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization.
Amir Rahimi*, Amirreza Shaban*, Thalaiyasingam Ajanthan, Richard Hartley, and Byron Boots.
European Conference on Computer Vision (ECCV), August 2020.
[pdf] [arxiv] [bib]

Similarity Learning for One-Shot Video Object Segmentation.
Mohammad Najafi*, Viveka Kulharia*, Thalaiyasingam Ajanthan, and Philip H.S. Torr.
CVPR Workshop: DAVIS Challenge on Video Object Segmentation, June 2018.
[pdf] [bib]