Iteratively reweighted graph cut for multi-label MRFs with non-convex priors. - Thalaiyasingam Ajanthan; Richard I. Hartley; Mathieu Salzmann; Hongdong Li (CVPR, 2015)

Efficient Linear Programming for Dense CRFs. - Thalaiyasingam Ajanthan; Alban Desmaison; Rudy Bunel; Mathieu Salzmann; Philip H. S. Torr; M. Pawan Kumar (CVPR, 2017)

A Semi-supervised Deep Generative Model for Human Body Analysis. - Rodrigo Andrade de Bem; Arnab Ghosh; Thalaiyasingam Ajanthan; Ondrej Miksik; N. Siddharth 001; Philip H. S. Torr (ECCV Workshops (2), 2018)

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence. - Arslan Chaudhry; Puneet Kumar Dokania; Thalaiyasingam Ajanthan; Philip H. S. Torr (ECCV (11), 2018)

DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis. - Rodrigo Andrade de Bem; Arnab Ghosh; Thalaiyasingam Ajanthan; Ondrej Miksik; N. Siddharth 001; Philip H. S. Torr (CoRR, 2018)

Generalized Range Moves. - Richard I. Hartley; Thalaiyasingam Ajanthan (CoRR, 2018)

Learning to Adapt for Stereo. - Alessio Tonioni; Oscar Rahnama; Thomas Joy; Luigi Di Stefano; Thalaiyasingam Ajanthan; Philip H. S. Torr (CVPR, 2019)

Proximal Mean-Field for Neural Network Quantization. - Thalaiyasingam Ajanthan; Puneet K. Dokania; Richard Hartley 001; Philip H. S. Torr (ICCV, 2019)

Snip: single-Shot Network Pruning based on Connection sensitivity. - Namhoon Lee; Thalaiyasingam Ajanthan; Philip H. S. Torr (ICLR (Poster), 2019)

A Conditional Deep Generative Model of People in Natural Images. - Rodrigo Andrade de Bem; Arnab Ghosh; Adnane Boukhayma; Thalaiyasingam Ajanthan; N. Siddharth 001; Philip H. S. Torr (WACV, 2019)

Continual Learning with Tiny Episodic Memories. - Arslan Chaudhry; Marcus Rohrbach; Mohamed Elhoseiny; Thalaiyasingam Ajanthan; Puneet Kumar Dokania; Philip H. S. Torr; Marc'Aurelio Ranzato (CoRR, 2019)

Fast and Differentiable Message Passing for Stereo Vision. - Zhiwei Xu; Thalaiyasingam Ajanthan; Richard Hartley 001 (CoRR, 2019)

Memory Efficient Max Flow for Multi-Label Submodular MRFs. - Thalaiyasingam Ajanthan; Richard Hartley 001; Mathieu Salzmann (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. - Thomas Joy; Alban Desmaison; Thalaiyasingam Ajanthan; Rudy Bunel; Mathieu Salzmann; Pushmeet Kohli; Philip H. S. Torr; M. Pawan Kumar (SIAM J. Imaging Sci., 2019)

RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs. - Zhiwei Xu; Thalaiyasingam Ajanthan; Vibhav Vineet; Richard I. Hartley (3DV, 2020)

Fast and Differentiable Message Passing on Pairwise Markov Random Fields. - Zhiwei Xu; Thalaiyasingam Ajanthan; Richard I. Hartley (ACCV (3), 2020)

Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization. - Amir Rahimi; Amirreza Shaban; Thalaiyasingam Ajanthan; Richard I. Hartley; Byron Boots (ECCV (24), 2020)

A Signal Propagation Perspective for Pruning Neural Networks at Initialization. - Namhoon Lee; Thalaiyasingam Ajanthan; Stephen Gould; Philip H. S. Torr (ICLR, 2020)

In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization. - Amir Rahimi; Amirreza Shaban; Thalaiyasingam Ajanthan; Richard Hartley 001; Byron Boots (CoRR, 2020)

Bidirectional Self-Normalizing Neural Networks. - Yao Lu; Stephen Gould; Thalaiyasingam Ajanthan (CoRR, 2020)

Post-hoc Calibration of Neural Networks. - Amir Rahimi; Kartik Gupta; Thalaiyasingam Ajanthan; Thomas Mensink; Cristian Sminchisescu; Richard Hartley 001 (CoRR, 2020)

Deep Learning Superpixel Semantic Segmentation with Transparent Initialization and Sparse Encoder. - Zhiwei Xu; Thalaiyasingam Ajanthan; Richard I. Hartley (CoRR, 2020)

DGPose: Deep Generative Models for Human Body Analysis. - Rodrigo Andrade de Bem; Arnab Ghosh; Thalaiyasingam Ajanthan; Ondrej Miksik; Adnane Boukhayma; N. Siddharth 001; Philip H. S. Torr (Int. J. Comput. Vis., 2020)

Mirror Descent View for Neural Network Quantization. - Thalaiyasingam Ajanthan; Kartik Gupta; Philip H. S. Torr; Richard Hartley 001; Puneet K. Dokania (AISTATS, 2021)

A Chaos Theory Approach to Understand Neural Network Optimization. - Michele Sasdelli; Thalaiyasingam Ajanthan; Tat-Jun Chin; Gustavo Carneiro 001 (DICTA, 2021)

Calibration of Neural Networks using Splines. - Kartik Gupta; Amir Rahimi; Thalaiyasingam Ajanthan; Thomas Mensink; Cristian Sminchisescu; Richard Hartley 001 (ICLR, 2021)

Understanding the effects of data parallelism and sparsity on neural network training. - Namhoon Lee; Thalaiyasingam Ajanthan; Philip H. S. Torr; Martin Jaggi (ICLR, 2021)

Improved Gradient-Based Adversarial Attacks for Quantized Networks. - Kartik Gupta; Thalaiyasingam Ajanthan (AAAI, 2022)

Retrieval Augmented Classification for Long-Tail Visual Recognition. - Alexander Long; Wei Yin 006; Thalaiyasingam Ajanthan; Vu Nguyen; Pulak Purkait; Ravi Garg; Alan Blair; Chunhua Shen; Anton van den Hengel (CVPR, 2022)

Training 1-Bit Networks on a Sphere: A Geometric Approach. - Luis Guerra; Thalaiyasingam Ajanthan; Gil Avraham; Yan Zou; Tom Drummond (ICANN (3), 2022)

Few-shot Weakly-Supervised Object Detection via Directional Statistics. - Amirreza Shaban; Amir Rahimi; Thalaiyasingam Ajanthan; Byron Boots; Richard Hartley 001 (WACV, 2022)

Understanding and Improving the Role of Projection Head in Self-Supervised Learning. - Kartik Gupta; Thalaiyasingam Ajanthan; Anton van den Hengel; Stephen Gould (CoRR, 2022)

Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. - Peixia Li; Pulak Purkait; Thalaiyasingam Ajanthan; Majid Abdolshah; Ravi Garg; Hisham Husain; Chenchen Xu; Stephen Gould; Wanli Ouyang; Anton van den Hengel (ICCV, 2023)

Adaptive Cross Batch Normalization for Metric Learning. - Thalaiyasingam Ajanthan; Matt Ma; Anton van den Hengel; Stephen Gould (CoRR, 2023)

Bidirectionally self-normalizing neural networks. - Yao Lu; Stephen Gould; Thalaiyasingam Ajanthan (Neural Networks, 2023)

Self-Supervision Improves Diffusion Models for Tabular Data Imputation. - Yixin Liu 001; Thalaiyasingam Ajanthan; Hisham Husain; Vu Nguyen 001 (CIKM, 2024)

Accept the Modality Gap: An Exploration in the Hyperbolic Space. - Sameera Ramasinghe; Violetta Shevchenko; Gil Avraham; Thalaiyasingam Ajanthan (CVPR, 2024)

Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame. - Evan Markou; Thalaiyasingam Ajanthan; Stephen Gould (NeurIPS, 2024)

Learning Visual Hierarchies with Hyperbolic Embeddings. - Ziwei Wang; Sameera Ramasinghe; Chenchen Xu; Julien Monteil; Loris Bazzani; Thalaiyasingam Ajanthan (CoRR, 2024)

Scaling Prompt Instructed Zero Shot Composed Image Retrieval with Image-Only Data. - Yiqun Duan; Sameera Ramasinghe; Stephen Gould; Thalaiyasingam Ajanthan (CoRR, 2025)

Nesterov Method for Asynchronous Pipeline Parallel Optimization. - Thalaiyasingam Ajanthan; Sameera Ramasinghe; Yan Zuo; Gil Avraham; Alexander Long (CoRR, 2025)

Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism. - Sameera Ramasinghe; Thalaiyasingam Ajanthan; Gil Avraham; Yan Zuo; Alexander Long (CoRR, 2025)

Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings. - Evan Markou; Thalaiyasingam Ajanthan; Stephen Gould (CoRR, 2025)