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Adrien Gaidon
Adrien Gaidon
Adjunct Professor, Stanford
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
15192019
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
12952016
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
6862020
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
5352019
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
4062020
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
3002020
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
2932019
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
2892021
Temporal Localization of Actions with Actoms
A Gaidon, Z Harchaoui, C Schmid
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
2672013
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
2532021
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
V Guizilini, R Hou, J Li, R Ambrus, A Gaidon
ICLR, 2020
2402020
Superdepth: Self-supervised, super-resolved monocular depth estimation
S Pillai, R Ambrus, A Gaidon
ICRA, 2019
2382019
Actom sequence models for efficient action detection
A Gaidon, Z Harchaoui, C Schmid
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2011
2162011
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
1892021
Differentiable rendering: A survey
H Kato, D Beker, M Morariu, T Ando, T Matsuoka, W Kehl, A Gaidon
arXiv preprint arXiv:2006.12057, 2020
1842020
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
1642017
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
1512020
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
1432022
Activity representation with motion hierarchies
A Gaidon, Z Harchaoui, C Schmid
International journal of computer vision 107, 219-238, 2014
1322014
SPIGAN: Privileged adversarial learning from simulation
KH Lee, G Ros, J Li, A Gaidon
ICLR, 2019
1302019
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