Computer Vision Applications

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.

Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups.
P. Li, P. Purkait, T. Ajanthan, M. Abdolshah, R. Garg, H. Husain, C. Xu, S. Gould, W. Ouyang, and A. van den Hengel.
International Conference on Computer Vision (ICCV), October 2023.
[pdf] [supp] [bib]

@article{li_dlg_iccv23,
  author = {Li, Peixia and Purkait, Pulak and Ajanthan, Thalaiyasingam and Abdolshah, Majid and Garg, Ravi and Husain, Hisham and Xu, Chenchen and Gould, Stephen and Ouyang, Wanli and van den Hengel, Anton},
  title = {Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups},
  journal = {ICCV},
  year = {2023}
}

Adaptive Cross Batch Normalization for Metric Learning.
T. Ajanthan, M. Ma, A. van den Hengel, and S. Gould.
arxiv:2303.17127, March 2023.
[pdf] [arxiv] [bib]

@article{ajanthan_axbn_arxiv23,
  author = {Ajanthan, Thalaiyasingam, and Ma, Matt, and van den Hengel, Anton, and Gould, Stephen},
  title = {Adaptive Cross Batch Normalization for Metric Learning},
  journal = {CoRR},
  year = {2023}
}

Retrieval Augmented Classification for Long-tail Visual Recognition.
A. Long, W. Yin, T. Ajanthan, V. Nguyen, P. Purkait, R. Garg. A. Blair, C. Shen, and A. van den Hengel.
Computer Vision and Pattern Recognition (CVPR), June 2022.
[pdf] [arxiv] [bib]

@article{long_rac_cvpr22,
  author = {Long, Alexander and Yin, Wei and Ajanthan, Thalaiyasingam and Nguyen, Vu and Purkait, Pulak and Garg, Ravi and Blair, Alan and Shen, Chunhua and van den Hengel, Anton},
  title = {Retrieval Augmented Classification for Long-Tail Visual Recognition},
  journal = {CVPR},
  year = {2022}
}

Provable Defense Against Clustering Attacks on 3D Point Clouds.
D. D. Denipitiyage, T. Ajanthan, P. Kamalaruban, and A. Weller.
AAAI Workshop: Adversarial Machine Learning and Beyond, November 2022.
[pdf] [bib]

@article{denipitiyage_pcrobust_aaai22,
  author = {Denipitiyage, Dishanika Dewani and Ajanthan, Thalaiyasingam and Kamalaruban, Parameswaran and Weller, Adrian},
  title = {Provable Defense Against Clustering Attacks on 3D Point Clouds},
  journal = {AAAI Workshop on Adversarial Machine Learning and Beyond},
  year = {2022}
}

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]

@article{xu_supseg_arxiv20,
  author = {Xu, Zhiwei, and Ajanthan, Thalaiyasingam, and Hartley, Richard},
  title = {Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder},
  journal = {CoRR},
  year = {2020}
}

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]

@article{rahimi_wsod_eccv20,
  author = {Rahimi, Amir, and Shaban, Amirreza, and Ajanthan, Thalaiyasingam, and Hartley, Richard, Boots, Byron},
  title = {Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization},
  journal = {ECCV},
  year = {2020}
}

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]

@article{najafi_vobjseg_davis2018,
  title={Similarity Learning for One-Shot Video Object Segmentation},
  author={Najafi, Mohammad and Kulharia, Viveka and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  journal={CVPR Workshop: DAVIS Challenge on Video Object Segmentation},
  year={2018}
}