Publications

Conferences

[new] A. Long, C. P. H. Koneputugodage, T. Ajanthan, Y. Zuo, G. Avraham, V. Shevchenko, H. M. Dolatabadi, and S. Ramasinghe, Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization, NeurIPS, 2025. [to appear] [bib]

@article{long_upms_neurips25,
  title={Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization}, 
  author={Long, Alexander and P Hewa Koneputugodage, Chamin and Ajanthan, Thalaiyasingam and Zuo, Yan and Avraham, Gil and Shevchenko, Violetta and Mohaghegh Dolatabadi, Hadi and Ramasinghe, Sameera},
  year={2025},
  journal={NeurIPS}
}

#PL

[new] S. Ramasinghe, T. Ajanthan, H. M. Dolatabadi, G. Avraham, V. Shevchenko, Y. Zuo, C. P. H. Koneputugodage, and A. Long, Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training, NeurIPS, 2025. [to appear] [bib]

@article{ramasinghe_mixtures_neurips25,
  title={Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training}, 
  author={Ramasinghe, Sameera and Ajanthan, Thalaiyasingam and Mohaghegh Dolatabadi, Hadi and Avraham, Gil and Shevchenko, Violetta and Zuo, Yan and P Hewa Koneputugodage, Chamin and Long, Alexander},
  year={2025},
  journal={NeurIPS}
}

#PL

[new] S. Ramasinghe, T. Ajanthan, G. Avraham, Y. Zuo, and A. Long, Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism, NeurIPS, 2025. [pdf] [arxiv] [bib]

@article{ramasinghe_protocol_neurips25,
  title={Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism}, 
  author={Ramasinghe, Sameera and Ajanthan, Thalaiyasingam and Avraham, Gil and Zuo, Yan and Long, Alexander},
  year={2025},
  journal={NeurIPS}
}

#PL

[new] E. Markou, T. Ajanthan, and S. Gould, Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings, NeurIPS, 2025. (spotlight) [pdf] [arxiv] [bib]

@article{markou_reduction_neurips25,
  author = {Markou, Evan, and Ajanthan, Thalaiyasingam, and Gould, Stephen},
  title = {Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings},
  journal = {NeurIPS},
  year = {2025}
}

#DLT

[new] Z. Wang, S. Ramasinghe, C. Xu, J. Monteil, L. Bazzani, and T. Ajanthan, Learning Visual Hierarchies with Hyperbolic Embeddings, ICCV, 2025. (oral) [pdf] [arxiv] [bib]

@article{wang_hier_iccv25,
  author = {Wang, Ziwei, and Ramasinghe, Sameera, and Xu, Chenchen, and Monteil, Julien, and Bazzani, Loris, and Ajanthan, Thalaiyasingam},
  title = {Learning Visual Hierarchies with Hyperbolic Embeddings},
  journal = {ICCV},
  year = {2025}
}

#CV

[new] T. Ajanthan, S. Ramasinghe, Y. Zuo, G. Avraham, and A. Long, Nesterov Method for Asynchronous Pipeline Parallel Optimization, ICML, 2025. [pdf] [arxiv] [code] [bib]

@article{ajanthan_asyncpp_icml25,
  author={Ajanthan, Thalaiyasingam and Ramasinghe, Sameera and Zuo, Yan and Avraham, Gil and Long, Alexander},
  title={Nesterov Method for Asynchronous Pipeline Parallel Optimization},
  journal={ICML},
  year={2025}
}

#PL

[new] Y. Duan, S. Ramasinghe, S. Gould, and T. Ajanthan, Scaling Prompt Instructed Zero Shot Composed Image Retrieval with Image-Only Data, IJCNN, 2025. [pdf] [arxiv] [bib]

@article{duan_instructcir_ijcnn25,
  author = {Duan, Yiqun, and Ramasinghe, Sameera, and Gould, Stephen and Ajanthan, Thalaiyasingam},
  title = {Scaling Prompt Instructed Zero Shot Composed Image Retrieval with Image-Only Data},
  journal = {IJCNN},
  year = {2025}
}

#VL

[new] E. Markou, T. Ajanthan, and S. Gould, Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame, NeurIPS, 2024. [pdf] [arxiv] [code] [bib]

@article{markou_gnc_neurips24,
  author = {Markou, Evan, and Ajanthan, Thalaiyasingam, and Gould, Stephen},
  title = {Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame},
  journal = {NeurIPS},
  year = {2024}
}

#DLT

[new] S. Ramasinghe, V. Shevchenko, G. Avraham, and T. Ajanthan, Accept the Modality Gap: An Exploration in the Hyperbolic Space, CVPR, 2024. (highlight) [pdf] [talk] [code] [bib]

@article{ramasinghe_atmg_cvpr24,
  author = {Ramasinghe, Sameera, and Shevchenko, Violetta, and Avraham, Gil, and Ajanthan, Thalaiyasingam},
  title = {Accept the Modality Gap: An Exploration in the Hyperbolic Space},
  journal = {CVPR},
  year = {2024}
}

#VL

P. Li, P. Purkait, T. Ajanthan, M. Abdolshah, R. Garg, H. Husain, C. Xu, S. Gould, W. Ouyang, and A. van den Hengel, Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups, ICCV, 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}
}

#CV

A. Long, W. Yin, T. Ajanthan, V. Nguyen, P. Purkait, R. Garg. A. Blair, C. Shen, and A. van den Hengel, Retrieval Augmented Classification for Long-tail Visual Recognition, CVPR, 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}
}

#CV

K. Gupta, and T. Ajanthan, Improved Gradient based Adversarial Attacks for Quantized Networks, AAAI, 2022. [pdf] [arxiv] [bib]

@article{gupta_iga_aaai22,
  author = {Gupta, Kartik and Ajanthan, Thalaiyasingam},
  title = {Improved Gradient based Adversarial Attacks for Quantized Networks},
  journal = {AAAI},
  year = {2022}
}

#NNQ, #SPNN

A. Shaban*, A. Rahimi*, T. Ajanthan, B. Boots, and R. Hartley, Few-shot Weakly-Supervised Object Detection via Directional Statistics, WACV, 2022. [pdf] [supp] [arxiv] [bib]

@article{rahimi_wsod_eccv20,
  author = {Shaban, Amirreza, and Rahimi, Amir, and Ajanthan, Thalaiyasingam, and Boots, Byron, and Hartley, Richard},
  title = {Few-shot Weakly-Supervised Object Detection via Directional Statistics},
  journal = {WACV},
  year = {2022}
}

#CV

M. Sasdelli, T. Ajanthan, T. J. Chin, and G. Carneiro, A Chaos Theory Approach to Understand Neural Network Optimization, DICTA, 2021. [pdf] [bib]

@article{sasdelli_chaos_dicta21,
  author = {Sasdelli, Michele, and Ajanthan, Thalaiyasingam, and Chin, Tat-Jun, and Carneiro, Gustavo},
  title = {A Chaos Theory Approach to Understand Neural Network Optimization},
  journal = {DICTA},
  year = {2021}
}

T. Ajanthan*, K. Gupta*, P. H. S. Torr, R. Hartley, and P. K. Dokania, Mirror Descent View for Neural Network Quantization, AISTATS, 2021. [pdf] [supp] [arxiv] [code] [bib]

@article{ajanthan_mdnnq_aistats21,
  author = {Ajanthan, Thalaiyasingam, and Gupta, Kartik, and Torr, Philip HS, and Hartley, Richard and Dokania, Puneet K},
  title = {Mirror Descent View for Neural Network Quantization},
  journal = {AISTATS},
  year = {2021}
}

#NNQ

K. Gupta, A, Rahimi, T. Ajanthan, T. Mensink, C. Sminchisescu, and R. Hartley, Calibration of Neural Networks using Splines, ICLR, 2021. [pdf] [arxiv] [bib]

@article{gupta_spline_iclr21,
  author = {Gupta, Kartik and Rahimi, Amir and Ajanthan, Thalaiyasingam and Mensink, Thomas and Sminchisescu and Cristian and Hartley, Richard},
  title = {Calibration of Neural Networks using Splines},
  journal = {ICLR},
  year = {2021}
}

#NNC

N. Lee, T. Ajanthan, P. H. S. Torr, and M. Jaggi, Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training, ICLR, 2021. [pdf] [arxiv] [bib]

@article{lee_dataparallelism_iclr21,
  author = {Lee, Namhoon and Ajanthan, Thalaiyasingam and Torr, Philip HS and Jaggi, Martin},
  title = {Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training},
  journal = {ICLR},
  year = {2021}
}

#NNP

Z. Xu, T. Ajanthan, V. Vineet, and R. Hartley, RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs, 3DV, 2020. (oral, best student paper) [pdf] [arxiv] [talk] [code] [bib]

@article{xu_ranp_3dv20,
  author = {Xu, Zhiwei, and Ajanthan, Thalaiyasingam, and Vineet, Vibhav, and Hartley, Richard},
  title = {RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs},
  journal = {3DV},
  year = {2020}
}

#NNP

Z. Xu, T. Ajanthan, and R. Hartley, Fast and Differentiable Message Passing on Pairwise Markov Random Fields, ACCV, 2020. (oral) [pdf] [arxiv] [talk] [code] [bib]

@article{xu_trwp_accv20,
  author = {Xu, Zhiwei, and Ajanthan, Thalaiyasingam, and Hartley, Richard},
  title = {Fast and Differentiable Message Passing on Pairwise Markov Random Fields},
  journal = {ACCV},
  year = {2020}
}

#MRF

A. Rahimi*, A. Shaban*, T. Ajanthan, R. Hartley, and B. Boots, Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization, ECCV, 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}
}

#CV

N. Lee, T. Ajanthan, S. Gould, and P. H. S. Torr, A Signal Propagation Perspective for Pruning Neural Networks at Initialization, ICLR, 2020. (spotlight) [pdf] [arxiv] [talk] [code] [bib]

@article{lee_disnip_iclr20,
  author = {Lee, Namhoon and Ajanthan, Thalaiyasingam and Gould, Stephen and Torr, Philip HS},
  title = {A Signal Propagation Perspective for Pruning Neural Networks at Initialization},
  journal = {ICLR},
  year = {2020}
}

#NNP, #SPNN

T. Ajanthan, P. K. Dokania, R. Hartley, and P. H. S. Torr, Proximal Mean-field for Neural Network Quantization, ICCV, 2019. [pdf] [supp] [arxiv] [poster] [talk] [code] [bib]

@article{ajanthan_pmf_iccv19,
  author = {Ajanthan, Thalaiyasingam, and Dokania, Puneet K and Hartley, Richard and Torr, Philip HS},
  title = {Proximal Mean-field for Neural Network Quantization},
  journal = {ICCV},
  year = {2019}
}

#NNQ

Alessio Tonioni, Oscar Rahnama*, Thomas Joy*, Luigi Di Stefano, T. Ajanthan, and P. H. S. Torr, Learning to Adapt for Stereo, CVPR, 2019. [pdf] [supp] [arxiv] [code] [bib]

@article{tonioni_l2a_cvpr19,
  author = {Tonioni, Alessio and Rahnama, Oscar and Joy, Thomas and Stefano, Luigi Di and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  title = {Learning to Adapt for Stereo},
  journal = {CVPR},
  year = {2019}
}

#META

N. Lee, T. Ajanthan, and P. H. S. Torr, SNIP: Single-shot Network Pruning based on Connection Sensitivity, ICLR, 2019. (>1000 citations) [pdf] [arxiv] [poster] [talk] [code] [bib]

@article{lee_snip_iclr19,
  author = {Lee, Namhoon and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  title = {{SNIP:} Single-shot Network Pruning based on Connection Sensitivity},
  journal = {ICLR},
  year = {2019}
}

#NNP

R. de Bem, A. Ghosh, A. Boukhayma, T. Ajanthan, N. Siddharth, and P. H. S. Torr, A Conditional Deep Generative Model of People in Natural Images, WACV, 2019. [pdf] [bib]

@article{debem_dgpose_wacv2019,
  title={A Conditional Deep Generative Model of People in Natural Images},
  author={De Bem, Rodrigo and Ghosh, Arnab and Boukhayma, Adnane and Ajanthan, Thalaiyasingam and and Narayanaswamy, Siddharth and Torr, Philip HS},
  journal={WACV},
  year={2019}
}

#GM

A. Chaudhry*, P. K. Dokania*, T. Ajanthan*, and P. H. S. Torr, Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence, ECCV, 2018. (>1000 citations) [pdf] [supp] [arxiv] [poster] [talk] [bib]

@article{chaudhry_rwalk_eccv2018,
  title={Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence},
  author={Chaudhry, Arslan and Dokania, Puneet K and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  journal={ECCV},
  year={2018}
}

#IL

T. Ajanthan, A. Desmaison, R. Bunel, M. Salzmann, P. H. S. Torr, and M. P. Kumar, Efficient Linear Programming for Dense CRFs, CVPR, 2017. [pdf] [supp] [arxiv] [poster] [talk] [code] [bib]

@article{ajanthan_proxlp_cvpr2017,
  title={Efficient linear programming for dense {CRFs}},
  author={Ajanthan, Thalaiyasingam and Desmaison, Alban and Bunel, Rudy and Salzmann, Mathieu and Torr, Philip HS and Kumar, M Pawan},
  journal={CVPR},
  year={2017}
}

#MRF

T. Ajanthan, R. Hartley, and M. Salzmann, Memory Efficient Max Flow for Multi-label Submodular MRFs, CVPR, 2016. [pdf] [supp] [poster] [talk] [code] [bib]

@article{ajanthan_memf_cvpr2016,
  title={Memory Efficient Max Flow for Multi-label Submodular {MRFs}},
  author={Ajanthan, Thalaiyasingam and Hartley, Richard and Salzmann, Mathieu},
  journal={CVPR},
  year={2016}
}

#MRF

T. Ajanthan, R. Hartley, M. Salzmann, and H. Li, Iteratively Reweighted Graph Cut for Multi-label MRFs with Non-convex Priors, CVPR, 2015. [pdf] [arxiv] [poster] [code] [bib]

@article{ajanthan_irgc_cvpr2015,
  title={Iteratively Reweighted Graph Cut for Multi-label {MRFs} with Non-convex Priors},
  author={Ajanthan, Thalaiyasingam and Hartley, Richard and Salzmann, Mathieu and Li, Hongdong},
  journal={CVPR},
  year={2015}
}

#MRF

T. Ajanthan, P Kamalaruban, and R. Rodrigo, Automatic Number Plate Recognition in Low Quality Videos, ICIIS, 2013. [pdf] [bib]

@article{ajanthan_anpr_iciis2013,
  title={Automatic Number Plate Recognition in Low Quality Videos},
  author={Ajanthan, Thalaiyasingam and Kamalaruban, Parameswaran and Rodrigo, Ranga},
  journal={ICIIS},
  year={2013}
}

Journals

Y. Lu, S. Gould, and T. Ajanthan, Bidirectionally Self-Normalizing Neural Networks, NN, 2023. [pdf] [arxiv] [talk] [bib]

@article{lu_bsnn_nn23,
  author = {Lu, Yao and Gould, Stephen and Ajanthan, Thalaiyasingam},
  title = {Bidirectionally Self-Normalizing Neural Networks},
  journal = {NN},
  year = {2023}
}

#DLT, #SPNN

R. de Bem, A. Ghosh, T. Ajanthan, O. Miksik, A. Boukhayma, N. Siddharth, and P. H. S. Torr, DGPose: Deep Generative Models for Human Body Analysis, IJCV, 2020. [pdf] [arxiv] [bib]

@article{debem_dgpose_ijcv2020,
  title={{DGPose:} Deep Generative Models for Human Body Analysis.},
  author={De Bem, Rodrigo and Ghosh, Arnab and Ajanthan, Thalaiyasingam and Miksik, Ondrej and Boukhayma, Adnane and Narayanaswamy, Siddharth and Torr, Philip HS},
  journal={IJCV},
  year={2020}
}

#GM

T. Joy*, A. Desmaison*, T. Ajanthan*, R. Bunel, M. Salzmann, P. Kohli, P. H. S. Torr, and M. P. Kumar, Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials, SIIMS, 2019. [pdf] [arxiv] [code] [bib]

@article{joy_higherorder_siam2018,
  title={Efficient Relaxations for Dense {CRFs} with Sparse Higher Order Potentials},
  author={Joy, Thomas and Desmaison, Alban and Ajanthan, Thalaiyasingam and Bunel, Rudy and Salzmann, Mathieu and Kohli, Pushmeet and Torr, Philip HS and Kumar, M Pawan},
  journal={SIIMS},
  year={2019}
}

#MRF

T. Ajanthan, R. Hartley, and M. Salzmann, Memory Efficient Max Flow for Multi-label Submodular MRFs, PAMI, 2018. [pdf] [arxiv] [talk] [code] [bib]

@article{ajanthan_memf_pami2018,
  title={Memory Efficient Max Flow for Multi-label Submodular {MRFs}},
  author={Ajanthan, Thalaiyasingam and Hartley, Richard and Salzmann, Mathieu},
  journal={PAMI},
  year={2018}
}

#MRF

Workshops

[new] S. Ramasinghe, T. Ajanthan, G. Avraham, Y. Zuo, and A. Long, Beyond Top-K: Structured Sparsification for Compression in Pipeline Parallel, ICLR Workshop: Modularity for Collaborative, Decentralized, and Continual Deep Learning, 2025. [pdf] [blog] [bib]

@article{ramasinghe_beyondtopk_iclr25,
  author={Ramasinghe, Sameera and Ajanthan, Thalaiyasingam and Avraham, Gil and Zuo, Yan and Long, Alexander},
  title={Beyond Top-K: Structured Sparsification for Compression in Pipeline Parallel},
  journal={ICLR},
  year={2025}
}

#PL

[new] Y. Zuo, G. Avraham, T. Ajanthan, S. Ramasinghe, and A. Long, Exploring Asynchronism in SWARM Parallelism, ICLR Workshop: Modularity for Collaborative, Decentralized, and Continual Deep Learning, 2025. [pdf] [blog] [bib]

@article{zuo_asyncswarm_iclr25,
  author={Zuo, Yan and Avraham, Gil and Ajanthan, Thalaiyasingam and Ramasinghe, Sameera and Long, Alexander},
  title={Exploring Asynchronism in SWARM Parallelism},
  journal={ICLR},
  year={2025}
}

#PL

[new] T. Ajanthan, S. Ramasinghe, G. Avraham, Y. Zuo, and A. Long, Momentum Look-Ahead for Asynchronous Distributed Low-Communication Training, ICLR Workshop: Modularity for Collaborative, Decentralized, and Continual Deep Learning, 2025. [pdf] [blog] [bib]

@article{ajanthan_mla_iclr25,
  author={Ajanthan, Thalaiyasingam and Ramasinghe, Sameera and Avraham, Gil and Zuo, Yan and Long, Alexander},
  title={Momentum Look-Ahead for Asynchronous Distributed Low-Communication Training},
  journal={ICLR},
  year={2025}
}

#PL

[new] Y. Liu, T. Ajanthan, H. Husain, and V. Nguyen, Self-Supervision Improves Diffusion Models for Tabular Data Imputation, ICLR Workshop: Generative Models for Decision Making, 2024. [pdf] [bib]

@article{liu_ssldm_iclr24,
  author = {Liu, Yixin, and Ajanthan, Thalaiyasingam, and Husain, Hisham, and Nguyen, Vu},
  title = {Self-Supervision Improves Diffusion Models for Tabular Data Imputation},
  journal = {ICLR Workshop: Generative Models for Decision Making},
  year = {2024}
}

A. Long, T. Ajanthan, and A. van den Hengel, Modality-Aware Adaptation of Contrastive Language-Image Models, ICLR Workshop: Mathematical and Empirical Understanding of Foundation Models, 2023. [pdf] [bib]

@article{long_mater_iclr23,
  author = {Long, Alexander, and Ajanthan, Thalaiyasingam, and van den Hengel, Anton},
  title = {Modality-Aware Adaptation of Contrastive Language-Image Models},
  journal = {ICLR Workshop: Mathematical and Empirical Understanding of Foundation Models},
  year = {2023}
}

#VL

K. Gupta, T. Ajanthan, A. van den Hengel, and S. Gould, Understanding and Improving the Role of Projection Head in Self-Supervised Learning, NeurIPS Workshop: Self-Supervised Learning - Theory and Practice, 2022. [pdf] [bib]

@article{gupta_ssl_neurips22,
  author = {Gupta, Kartik, and Ajanthan, Thalaiyasingam, and van den Hengel, Anton, and Gould, Stephen},
  title = {Understanding and Improving the Role of Projection Head in Self-Supervised Learning},
  journal = {NeurIPS Workshop: Self-Supervised Learning - Theory and Practice},
  year = {2022}
}

D. D. Denipitiyage, T. Ajanthan, P. Kamalaruban, and A. Weller, Provable Defense Against Clustering Attacks on 3D Point Clouds, AAAI Workshop: Adversarial Machine Learning and Beyond, 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}
}

#CV

A. Chaudhry, M. Rohrbach, M. Elhoseiny, T. Ajanthan, P. K. Dokania, P. H. S. Torr, and M. Ranzato, Continual Learning with Tiny Episodic Memories, ICML Workshop: Multi-Task and Lifelong Reinforcement Learning, 2019. (>1000 citations) [pdf] [arxiv] [bib]

@article{Chaudhry_tinyer_icml2019,
  title={Continual Learning with Tiny Episodic Memories},
  author={Chaudhry, Arslan and Rohrbach, Marcus and Elhoseiny, Mohamed and Ajanthan, Thalaiyasingam and Dokania, Puneet K and Torr, Philip HS and Ranzato, Marc'Aurelio},
  journal={ICML Workshop: Multi-Task and Lifelong Reinforcement Learning},
  year={2019}
}

#IL

M. Najafi*, V. Kulharia*, T. Ajanthan, and P. H. S. Torr, Similarity Learning for One-Shot Video Object Segmentation, CVPR Workshop: DAVIS Challenge on Video Object Segmentation, 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}
}

#META, #CV

Thesis

T. Ajanthan, Optimization of Markov Random Fields in Computer Vision, PhD Thesis, 2017. [pdf] [talk] [bib]

@article{ajanthan_thesis_2017,
  title={Optimization of {M}arkov Random Fields in Computer Vision},
  author={Ajanthan, Thalaiyasingam},
  journal={ANU PhD Thesis},
  year={2017}
}

#MRF

Technical Reports

T. Ajanthan, M. Ma, A. van den Hengel, and S. Gould, Adaptive Cross Batch Normalization for Metric Learning, arxiv:2303.17127, 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}
}

#CV

Z. Xu, T. Ajanthan, R. Hartley, Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder, arxiv:2010.04363, 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}
}

#CV

A, Rahimi, K. Gupta, T. Ajanthan, T. Mensink, C. Sminchisescu, and R. Hartley, Post-hoc Calibration of Neural Networks, arxiv:2006.12807, 2020. [pdf] [arxiv] [bib]

@article{rahimi_glayers_arxiv20,
  author = {Rahimi, Amir and Gupta, Kartik and Ajanthan, Thalaiyasingam and Mensink, Thomas and Sminchisescu and Cristian and Hartley, Richard},
  title = {Post-hoc Calibration of Neural Networks},
  journal = {CoRR},
  year = {2020}
}

#NNC

R. Hartley, and T. Ajanthan, Generalized Range Moves, arXiv:1811.09171, 2018. [pdf] [arxiv] [bib]

@article{hartley_gswap_arxiv18,
  author = {Hartley, Richard and Ajanthan, Thalaiyasingam},
  title = {Generalized Range Moves},
  journal = {CoRR},
  year = {2018}
}

#MRF