Snip: Single-shot network pruning based on connection sensitivity N Lee, T Ajanthan, PHS Torr arXiv preprint arXiv:1810.02340, 2018 | 1167 | 2018 |
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence A Chaudhry, PK Dokania, T Ajanthan, PHS Torr European Conference on Computer Vision (ECCV), 2018 | 1139 | 2018 |
Continual learning with tiny episodic memories A Chaudhry, M Rohrbach, M Elhoseiny, T Ajanthan, P Dokania, P Torr, ... Workshop on Multi-Task and Lifelong Reinforcement Learning, 2019 | 820* | 2019 |
A signal propagation perspective for pruning neural networks at initialization N Lee, T Ajanthan, S Gould, PHS Torr arXiv preprint arXiv:1906.06307, 2019 | 169 | 2019 |
Calibration of neural networks using splines K Gupta, A Rahimi, T Ajanthan, T Mensink, C Sminchisescu, R Hartley arXiv preprint arXiv:2006.12800, 2020 | 110 | 2020 |
Learning to adapt for stereo A Tonioni, O Rahnama, T Joy, LD Stefano, T Ajanthan, PHS Torr Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 84 | 2019 |
Retrieval augmented classification for long-tail visual recognition A Long, W Yin, T Ajanthan, V Nguyen, P Purkait, R Garg, A Blair, C Shen, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 77 | 2022 |
Mirror descent view for neural network quantization T Ajanthan, K Gupta, P Torr, R Hartley, P Dokania International conference on artificial intelligence and statistics, 2809-2817, 2021 | 23 | 2021 |
Understanding and improving the role of projection head in self-supervised learning K Gupta, T Ajanthan, A Hengel, S Gould arXiv preprint arXiv:2212.11491, 2022 | 21 | 2022 |
A conditional deep generative model of people in natural images R De Bem, A Ghosh, A Boukhayma, T Ajanthan, N Siddharth, P Torr 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1449-1458, 2019 | 21 | 2019 |
Efficient Linear Programming for Dense CRFs T Ajanthan, A Desmaison, R Bunel, M Salzmann, PHS Torr, MP Kumar The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 | 21 | 2017 |
Proximal mean-field for neural network quantization T Ajanthan, PK Dokania, R Hartley, PHS Torr Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 19 | 2019 |
Bidirectionally self-normalizing neural networks Y Lu, S Gould, T Ajanthan Neural Networks 167, 283-291, 2023 | 18 | 2023 |
Improved gradient-based adversarial attacks for quantized networks K Gupta, T Ajanthan Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6810-6818, 2022 | 18 | 2022 |
Automatic Number Plate Recognition in Low Quality Videos T Ajanthan, P Kamalaruban, R Rodrigo Industrial and Information Systems (ICIIS), 566 - 571, 2013 | 18 | 2013 |
A Semi-supervised Deep Generative Model for Human Body Analysis R de Bem, A Ghosh, T Ajanthan, O Miksik, N Siddharth, P Torr ECCV Workshop on Human Behaviour Understanding, 2018 | 17 | 2018 |
Pairwise similarity knowledge transfer for weakly supervised object localization A Rahimi, A Shaban, T Ajanthan, R Hartley, B Boots European conference on computer vision, 395-412, 2020 | 16 | 2020 |
Dgpose: Deep generative models for human body analysis R de Bem, A Ghosh, T Ajanthan, O Miksik, A Boukhayma, N Siddharth, ... International Journal of Computer Vision 128, 1537-1563, 2020 | 16 | 2020 |
Iteratively Reweighted Graph Cut for Multi-Label MRFs With Non-Convex Priors T Ajanthan, R Hartley, M Salzmann, H Li The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5144-5152, 2015 | 14 | 2015 |
Post-hoc calibration of neural networks A Rahimi, K Gupta, T Ajanthan, T Mensink, C Sminchisescu, R Hartley arXiv preprint arXiv:2006.12807 2, 2020 | 13 | 2020 |