2024 |
Li, P., Purkait, P., Ajanthan, T., Abdolshah, M., Garg, R., Husain, H., . . . Van Den Hengel, A. (2024). Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023 ) (pp. 1229-1238). online: IEEE. DOI |
2024 |
Silva, A., Moskvyak, O., Long, A., Garg, R., Gould, S., Avraham, G., & Van Den Hengel, A. (2024). LipAT: Beyond Style Transfer for Controllable Neural Simulation of Lipstick using Cosmetic Attributes. In Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 (pp. 8031-8040). 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 Scopus30 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 Scopus17 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 Scopus30 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 Scopus19 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 Scopus520 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. Scopus38 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 Scopus20 |
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 Scopus950 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 Scopus33 |
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 |