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ORCIDThalaiyasingam Ajanthan
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2020 – today
- 2024
[c24]Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen:
Self-Supervision Improves Diffusion Models for Tabular Data Imputation. CIKM 2024: 1513-1522- [c23]Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Thalaiyasingam Ajanthan:
Accept the Modality Gap: An Exploration in the Hyperbolic Space. CVPR 2024: 27253-27262 - [i27]Yixin Liu, Thalaiyasingam Ajanthan, Hisham Husain, Vu Nguyen:
Self-Supervision Improves Diffusion Models for Tabular Data Imputation. CoRR abs/2407.18013 (2024) - 2023
- [j4]Yao Lu
, Stephen Gould, Thalaiyasingam Ajanthan:
Bidirectionally self-normalizing neural networks. Neural Networks 167: 283-291 (2023) - [c22]Peixia Li, Pulak Purkait, Thalaiyasingam Ajanthan, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang, Anton van den Hengel:
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. ICCV 2023: 1229-1238 - [i26]Thalaiyasingam Ajanthan, Matt Ma, Anton van den Hengel, Stephen Gould:
Adaptive Cross Batch Normalization for Metric Learning. CoRR abs/2303.17127 (2023) - 2022
- [c21]Kartik Gupta, Thalaiyasingam Ajanthan:
Improved Gradient-Based Adversarial Attacks for Quantized Networks. AAAI 2022: 6810-6818 - [c20]Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel:
Retrieval Augmented Classification for Long-Tail Visual Recognition. CVPR 2022: 6949-6959 - [c19]Luis Guerra, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zou, Tom Drummond:
Training 1-Bit Networks on a Sphere: A Geometric Approach. ICANN (3) 2022: 789-802 - [c18]Amirreza Shaban, Amir Rahimi, Thalaiyasingam Ajanthan, Byron Boots, Richard Hartley:
Few-shot Weakly-Supervised Object Detection via Directional Statistics. WACV 2022: 1040-1049 - [i25]Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel:
Retrieval Augmented Classification for Long-Tail Visual Recognition. CoRR abs/2202.11233 (2022) - [i24]Kartik Gupta, Thalaiyasingam Ajanthan, Anton van den Hengel, Stephen Gould:
Understanding and Improving the Role of Projection Head in Self-Supervised Learning. CoRR abs/2212.11491 (2022) - 2021
- [c17]Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania:
Mirror Descent View for Neural Network Quantization. AISTATS 2021: 2809-2817 - [c16]Michele Sasdelli, Thalaiyasingam Ajanthan, Tat-Jun Chin, Gustavo Carneiro:
A Chaos Theory Approach to Understand Neural Network Optimization. DICTA 2021: 1-10 - [c15]Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley:
Calibration of Neural Networks using Splines. ICLR 2021 - [c14]Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr, Martin Jaggi:
Understanding the effects of data parallelism and sparsity on neural network training. ICLR 2021 - [i23]Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet, Richard I. Hartley:
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs. CoRR abs/2103.08457 (2021) - [i22]Amirreza Shaban, Amir Rahimi, Thalaiyasingam Ajanthan, Byron Boots, Richard I. Hartley:
Few-shot Weakly-Supervised Object Detection via Directional Statistics. CoRR abs/2103.14162 (2021) - 2020
- [j3]Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip H. S. Torr:
DGPose: Deep Generative Models for Human Body Analysis. Int. J. Comput. Vis. 128(5): 1537-1563 (2020) - [c13]Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet, Richard I. Hartley:
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs. 3DV 2020: 180-189 - [c12]Zhiwei Xu, Thalaiyasingam Ajanthan, Richard I. Hartley:
Fast and Differentiable Message Passing on Pairwise Markov Random Fields. ACCV (3) 2020: 523-540 - [c11]Amir Rahimi, Amirreza Shaban, Thalaiyasingam Ajanthan, Richard I. Hartley, Byron Boots:
Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization. ECCV (24) 2020: 395-412 - [c10]Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr:
A Signal Propagation Perspective for Pruning Neural Networks at Initialization. ICLR 2020 - [i21]Amir Rahimi, Amirreza Shaban, Thalaiyasingam Ajanthan, Richard Hartley, Byron Boots:
In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization. CoRR abs/2003.08375 (2020) - [i20]Kartik Gupta, Thalaiyasingam Ajanthan:
Improved Gradient based Adversarial Attacks for Quantized Networks. CoRR abs/2003.13511 (2020) - [i19]Yao Lu, Stephen Gould, Thalaiyasingam Ajanthan:
Bidirectional Self-Normalizing Neural Networks. CoRR abs/2006.12169 (2020) - [i18]Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley:
Calibration of Neural Networks using Splines. CoRR abs/2006.12800 (2020) - [i17]Amir Rahimi, Kartik Gupta, Thalaiyasingam Ajanthan, Thomas Mensink, Cristian Sminchisescu, Richard Hartley:
Post-hoc Calibration of Neural Networks. CoRR abs/2006.12807 (2020) - [i16]Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet, Richard I. Hartley:
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs. CoRR abs/2010.02488 (2020) - [i15]Zhiwei Xu, Thalaiyasingam Ajanthan, Richard I. Hartley:
Deep Learning Superpixel Semantic Segmentation with Transparent Initialization and Sparse Encoder. CoRR abs/2010.04363 (2020)
2010 – 2019
- 2019
- [j2]Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann:
Memory Efficient Max Flow for Multi-Label Submodular MRFs. IEEE Trans. Pattern Anal. Mach. Intell. 41(4): 886-900 (2019) - [j1]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [c9]Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Learning to Adapt for Stereo. CVPR 2019: 9661-9670 - [c8]Thalaiyasingam Ajanthan, Puneet K. Dokania, Richard Hartley, Philip H. S. Torr:
Proximal Mean-Field for Neural Network Quantization. ICCV 2019: 4870-4879 - [c7]Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Snip: single-Shot Network Pruning based on Connection sensitivity. ICLR (Poster) 2019 - [c6]Rodrigo Andrade de Bem, Arnab Ghosh, Adnane Boukhayma, Thalaiyasingam Ajanthan, N. Siddharth, Philip H. S. Torr:
A Conditional Deep Generative Model of People in Natural Images. WACV 2019: 1449-1458 - [i14]Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Philip H. S. Torr, Marc'Aurelio Ranzato:
Continual Learning with Tiny Episodic Memories. CoRR abs/1902.10486 (2019) - [i13]Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Learning to Adapt for Stereo. CoRR abs/1904.02957 (2019) - [i12]Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr:
A Signal Propagation Perspective for Pruning Neural Networks at Initialization. CoRR abs/1906.06307 (2019) - [i11]Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania:
Mirror Descent View for Neural Network Quantization. CoRR abs/1910.08237 (2019) - [i10]Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley:
Fast and Differentiable Message Passing for Stereo Vision. CoRR abs/1910.10892 (2019) - 2018
- [c5]Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr:
A Semi-supervised Deep Generative Model for Human Body Analysis. ECCV Workshops (2) 2018: 500-517 - [c4]Arslan Chaudhry, Puneet Kumar Dokania, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. ECCV (11) 2018: 556-572 - [i9]Arslan Chaudhry, Puneet Kumar Dokania, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. CoRR abs/1801.10112 (2018) - [i8]Rodrigo Andrade de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, N. Siddharth, Philip H. S. Torr:
DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis. CoRR abs/1804.06364 (2018) - [i7]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials. CoRR abs/1805.09028 (2018) - [i6]Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr:
SNIP: Single-shot Network Pruning based on Connection Sensitivity. CoRR abs/1810.02340 (2018) - [i5]Richard I. Hartley, Thalaiyasingam Ajanthan:
Generalized Range Moves. CoRR abs/1811.09171 (2018) - [i4]Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Richard I. Hartley, Philip H. S. Torr:
Proximal Mean-field for Neural Network Quantization. CoRR abs/1812.04353 (2018) - 2017
- [c3]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CVPR 2017: 2934-2942 - [i3]Thalaiyasingam Ajanthan, Richard I. Hartley, Mathieu Salzmann:
Memory Efficient Max Flow for Multi-label Submodular MRFs. CoRR abs/1702.05888 (2017) - 2016
- [c2]Thalaiyasingam Ajanthan, Richard I. Hartley, Mathieu Salzmann:
Memory Efficient Max Flow for Multi-label Submodular MRFs. CVPR 2016: 5867-5876 - [i2]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CoRR abs/1611.09718 (2016) - 2015
- [c1]Thalaiyasingam Ajanthan, Richard I. Hartley, Mathieu Salzmann, Hongdong Li:
Iteratively reweighted graph cut for multi-label MRFs with non-convex priors. CVPR 2015: 5144-5152 - 2014
- [i1]Thalaiyasingam Ajanthan, Richard I. Hartley, Mathieu Salzmann, Hongdong Li:
Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors. CoRR abs/1411.6340 (2014)
Coauthor Index
aka: Philip H. S. Torr
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