| Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss K Cao, C Wei, A Gaidon, N Arechiga, T Ma Advances in Neural Information Processing Systems, 2019 | 1441 | 2019 |
| Virtual worlds as proxy for multi-object tracking analysis A Gaidon, Q Wang, Y Cabon, E Vig Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 1252 | 2016 |
| 3D packing for self-supervised monocular depth estimation V Guizilini, R Ambrus, S Pillai, A Raventos, A Gaidon Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 642 | 2020 |
| Exploring the Limitations of Behavior Cloning for Autonomous Driving F Codevilla, E Santana, AM López, A Gaidon Proceedings of the IEEE International Conference on Computer Vision, 2019 | 501 | 2019 |
| It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction K Mangalam, H Girase, S Agarwal, KH Lee, E Adeli, J Malik, A Gaidon European Conference on Computer Vision (ECCV), 2020 | 381 | 2020 |
| Roi-10d: Monocular lifting of 2d detection to 6d pose and metric shape F Manhardt, W Kehl, A Gaidon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 287 | 2019 |
| Spatio-Temporal Graph for Video Captioning with Knowledge Distillation B Pan, H Cai, DA Huang, KH Lee, A Gaidon, E Adeli, JC Niebles Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 285 | 2020 |
| Is pseudo-lidar needed for monocular 3d object detection? D Park, R Ambrus, V Guizilini, J Li, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 270 | 2021 |
| Temporal Localization of Actions with Actoms A Gaidon, Z Harchaoui, C Schmid IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 | 266 | 2013 |
| Provable guarantees for self-supervised deep learning with spectral contrastive loss JZ HaoChen, C Wei, A Gaidon, T Ma Advances in Neural Information Processing Systems 34, 5000-5011, 2021 | 237 | 2021 |
| Semantically-Guided Representation Learning for Self-Supervised Monocular Depth V Guizilini, R Hou, J Li, R Ambrus, A Gaidon ICLR, 2020 | 234 | 2020 |
| Superdepth: Self-supervised, super-resolved monocular depth estimation S Pillai, R Ambrus, A Gaidon ICRA, 2019 | 231 | 2019 |
| Actom sequence models for efficient action detection A Gaidon, Z Harchaoui, C Schmid Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2011 | 216 | 2011 |
| Learning to track with object permanence P Tokmakov, J Li, W Burgard, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 177 | 2021 |
| Differentiable rendering: A survey H Kato, D Beker, M Morariu, T Ando, T Matsuoka, W Kehl, A Gaidon arXiv preprint arXiv:2006.12057, 2020 | 170 | 2020 |
| Procedural generation of videos to train deep action recognition networks C Roberto de Souza, A Gaidon, Y Cabon, A Manuel Lopez Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 159 | 2017 |
| Spatiotemporal relationship reasoning for pedestrian intent prediction B Liu, E Adeli, Z Cao, KH Lee, A Shenoi, A Gaidon, JC Niebles IEEE Robotics and Automation Letters 5 (2), 3485-3492, 2020 | 146 | 2020 |
| Self-supervised Learning is More Robust to Dataset Imbalance H Liu, JZ HaoChen, A Gaidon, T Ma International Conference on Learning Representations (ICLR'22), 2022 | 132 | 2022 |
| Activity representation with motion hierarchies A Gaidon, Z Harchaoui, C Schmid International journal of computer vision 107, 219-238, 2014 | 132 | 2014 |
| SPIGAN: Privileged adversarial learning from simulation KH Lee, G Ros, J Li, A Gaidon ICLR, 2019 | 124 | 2019 |
Rares AmbrusToyota Research Institute (TRI)Verified email at tri.global
