| 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 | 1064 | 2016 |
| 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 | 934 | 2019 |
| 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 | 428 | 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 | 346 | 2019 |
| Temporal Localization of Actions with Actoms A Gaidon, Z Harchaoui, C Schmid IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 | 254 | 2013 |
| 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 | 244 | 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 | 224 | 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 | 214 | 2011 |
| 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 | 203 | 2020 |
| Superdepth: Self-supervised, super-resolved monocular depth estimation S Pillai, R Ambrus, A Gaidon ICRA, 2019 | 192 | 2019 |
| Semantically-Guided Representation Learning for Self-Supervised Monocular Depth V Guizilini, R Hou, J Li, R Ambrus, A Gaidon ICLR, 2020 | 158 | 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 | 140 | 2021 |
| 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 | 140* | 2017 |
| 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 | 136 | 2021 |
| Activity representation with motion hierarchies A Gaidon, Z Harchaoui, C Schmid International journal of computer vision 107, 219-238, 2014 | 129 | 2014 |
| Differentiable rendering: A survey H Kato, D Beker, M Morariu, T Ando, T Matsuoka, W Kehl, A Gaidon arXiv preprint arXiv:2006.12057, 2020 | 111 | 2020 |
| Learning to fuse things and stuff J Li, A Raventos, A Bhargava, T Tagawa, A Gaidon arXiv preprint arXiv:1812.01192, 2018 | 110 | 2018 |
| INRIA-LEARs video copy detection system M Douze, A Gaidon, H Jegou, M Marszałek, C Schmid TREC Video Retrieval Evaluation (TRECVID Workshop), 2008 | 103 | 2008 |
| 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 | 99 | 2021 |
| Recognizing activities with cluster-trees of tracklets A Gaidon, Z Harchaoui, C Schmid British Machine Vision Conference (BMVC), 2012 | 96 | 2012 |
Rares AmbrusToyota Research Institute (TRI)Verified email at tri.global
