Ravi Garg

Australian Institute for Machine Learning - Operations

Division of Research and Innovation

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

I am a Computer Vision and Machine Learning Researcher and a Future Making Fellow at Australian Institute of Machine Learning at The University of Adelaide. I obtained my PhD from The University of London in 2014 with the dissertation on "Dense motion capture of deformable surfaces from monocular video", under the guidance of Prof. Lourdes Agapito. I have worked for six years as Sr. Research Associate with Prof Ian Reid at The University of Adelaide. My research incorporated Machine Learning into real-time Localisation and Mapping and introduced Unsupervised Learning frameworks for single view depth and egomotion estimation. Before joining the university as Future Making Fellow, I was working as Sr. Applied Scientist for 2.5 years at Amazon Research with Prof Anton Van Den Hengel.

I am currently interested in 3D Reconstruction, Visual Reasoning and Generative AI for Robotics and Automation.

Career
Publications
Supervision
Contact

Appointments

Date Position Institution name
2023 - ongoing Future Making Fellow University of Adelaide
2020 - 2023 Sr. Applied Scientist Amazon
2014 - 2020 Sr. Research Associate The University of Adelaide
Education

Date Institution name Country Title
2009 - 2014 Queen Mary University of London United Kingdom PhD
Research Interests

Artificial Intelligence Computer Vision

Date	Position	Institution name
2023 - ongoing	Future Making Fellow	University of Adelaide
2020 - 2023	Sr. Applied Scientist	Amazon
2014 - 2020	Sr. Research Associate	The University of Adelaide

Date	Institution name	Country	Title
2009 - 2014	Queen Mary University of London	United Kingdom	PhD

Journals

Year	Citation
2021	Lassance, C., Latif, Y., Garg, R., Gripon, V., & Reid, I. (2021). Improved visual localization via graph filtering. Journal of Imaging, 7(2), 20. DOI Scopus1 WoS1
2016	Garg, R., Eriksson, A., & Reid, I. (2016). Non-linear Dimensionality Regularizer for Solving Inverse Problems. arXiv preprint arXiv:1603.05015.
2013	Garg, R., Roussos, A., & Agapito, L. (2013). A variational approach to video registration with subspace constraints. International Journal of Computer Vision, 104(3), 286-314. DOI Scopus108

Conference Papers

Year	Citation
2023	Li, P., Purkait, P., Ajanthan, T., Abdolshah, M., Garg, R., Husain, H., . . . Van Den Hengel, A. (2023). Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1229-1238). Online: IEEE. DOI
2022	Long, A., Yin, W., Ajanthan, T., Nguyen, V., Purkait, P., Garg, R., . . . Van Den Hengel, A. (2022). Retrieval Augmented Classification for Long-Tail Visual Recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2022-June (pp. 6949-6959). Online: IEEE. DOI Scopus24 WoS2
2020	Li, K., Garg, R., Cai, M., & Reid, I. (2020). Single-view object shape reconstruction using deep shape prior and silhouette. In 30th British Machine Vision Conference 2019, BMVC 2019 (pp. 1-14). online: BMVA. Scopus3
2019	Weerasekera, C. S., Garg, R., Latif, Y., & Reid, I. (2019). Learning deeply supervised good features to match for dense monocular reconstruction. In Proceedings of the 14th Asian Conference on Computer Vision (ACCV 2018), as published in Lecture Notes in Computer Science Vol. 11365 (pp. 609-624). Switzerland: Springer. DOI Scopus3 WoS2
2019	Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. (2019). Self-supervised learning for single view depth and surface normal estimation. In Proceedings of the 2019 IEEE International Conference on Robotics and Automation (ICRA) Vol. 2019-May (pp. 4811-4817). Piscataway, NJ.: IEEE. DOI Scopus15 WoS13
2018	Latif, Y., Garg, R., Milford, M., & Reid, I. (2018). Addressing challenging place recognition tasks using generative adversarial networks. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2349-2355). Piscataway, NJ.: IEEE. DOI Scopus29 WoS22
2018	Weerasekera, C. S., Dharmasiri, T., Garg, R., Drummond, T., & Reid, I. (2018). Just-in-time reconstruction: Inpainting sparse maps using single view depth predictors as priors. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 4977-4984). Piscataway, NJ.: IEEE. DOI Scopus18 WoS13
2018	Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M. (2018). Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 340-349). Piscataway, NJ.: IEEE. DOI Scopus491 WoS341
2017	Milan, A., Rezatofighi, S., Garg, R., Dick, A., & Reid, I. (2017). Data-driven approximations to NP-hard problems. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 1453-1459). San Francisco: AAAI. Scopus36 WoS19
2017	Ji, P., Reid., Garg., H. Li., & M. Salzmann. (2017). Low-Rank Kernel Subspace Clustering. In https://arxiv.org/pdf/1707.04974.pdf. https://arxiv.org/pdf/1707.04974.pdf.
2017	Weerasekera, C., Latif, Y., Garg, R., & Reid, I. (2017). Dense monocular reconstruction using surface normals. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2524-2531). online: IEEE. DOI Scopus19
2017	Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE. DOI Scopus31 WoS23
2016	Garg, R., Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: geometry to the rescue. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9912 LNCS (pp. 740-756). Amsterdam, Netherlands: Springer International Publishing. DOI Scopus901 WoS855
2013	Garg, R., Roussos, A., & Agapito, L. (2013). Dense variational reconstruction of non-rigid surfaces from monocular video. In Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA (pp. 1272-1279). Portland, Oregon: IEEE. DOI Scopus172
2012	Roussos, A., Russell, C., Garg, R., & Agapito, L. (2012). Dense multibody motion estimation and reconstruction from a handheld camera. In 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2012) (pp. 31-40). Atlanta, Georgia: Institute of Electrical and Electronics Engineers. DOI Scopus32
2011	Julià, C., Paladini, M., Garg, R., Puig, D., & Agapito, L. (2011). Automatic estimation of the number of deformation modes in non-rigid SfM with missing data. In Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings Vol. 6688 LNCS (pp. 381-392). Ystad, Sweden: Springer. DOI
2011	Garg, R., Roussos, A., & Agapito, L. (2011). Robust trajectory-space TV-L1 optical flow for non-rigid sequences. In Y. Boykov, F. Kahl, V. Lempitsky, & F. Schmidt (Eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition,: proceedings 8th International Conference, EMMCVPR 2011 Vol. 6819 LNCS (pp. 300-314). St. Petersburg, Russia: Springer. DOI Scopus19
2010	Garg, R., Pizarro, L., Rueckert, D., & Agapito, L. (2010). Dense multi-frame optic flow for non-rigid objects using subspace constraints. In Computer Vision – ACCV 2010 Vol. 6495 LNCS (pp. 460-473). Queenstown, New Zealand: Springer. DOI Scopus20
2010	Garg, R., Sahu, R., Mousset, S., & Bensrhair, A. (2010). Obstacle detection for vehicle navigation by chaining of adoptive declivities using geometrical constrains. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 7546. SPIE. DOI

Current Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2024	Co-Supervisor	Understanding Generalization and Memorization of Deep Neural Network from Distance and Rank	Doctor of Philosophy	Doctorate	Full Time	Mr Jianqiao Zheng

Past Higher Degree by Research Supervision (University of Adelaide)

Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
2017 - 2018	Co-Supervisor	Deeply Learned Priors for Geometric Reconstruction	Doctor of Philosophy	Doctorate	Full Time	Mr Saroj Weerasekera

Email: ravi.garg@adelaide.edu.au

Ravi Garg

Appointments

Education

Research Interests

Journals

Conference Papers

Current Higher Degree by Research Supervision (University of Adelaide)

Past Higher Degree by Research Supervision (University of Adelaide)

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