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2020 – today
- 2022
[i6]Dongxu Guo, Taylor Mordan, Alexandre Alahi:
Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion. CoRR abs/2203.02489 (2022)- 2021
- [c6]Behnam Parsaeifard, Saeed Saadatnejad, Yuejiang Liu, Taylor Mordan, Alexandre Alahi:
Learning Decoupled Representations for Human Pose Forecasting. ICCVW 2021: 2294-2303 - [c5]Lorenzo Bertoni, Sven Kreiss, Taylor Mordan
, Alexandre Alahi:
MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization. ICRA 2021: 5126-5132 - [c4]Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi:
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? NeurIPS 2021: 21808-21820 - [i5]Younes Belkada, Lorenzo Bertoni, Romain Caristan, Taylor Mordan, Alexandre Alahi:
Do Pedestrians Pay Attention? Eye Contact Detection in the Wild. CoRR abs/2112.04212 (2021) - [i4]Saeed Saadatnejad, Siyuan Li, Taylor Mordan, Alexandre Alahi:
A Shared Representation for Photorealistic Driving Simulators. CoRR abs/2112.05134 (2021) - 2020
- [i3]Lorenzo Bertoni, Sven Kreiss, Taylor Mordan, Alexandre Alahi:
MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization. CoRR abs/2008.10913 (2020) - [i2]Taylor Mordan, Matthieu Cord, Patrick Pérez, Alexandre Alahi:
Detecting 32 Pedestrian Attributes for Autonomous Vehicles. CoRR abs/2012.02647 (2020)
2010 – 2019
- 2019
- [j1]Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord:
End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection. Int. J. Comput. Vis. 127(11-12): 1659-1679 (2019) - 2018
- [b1]Taylor Mordan:
Designing Deep Architectures for Visual Understanding. (Conception d'architectures profondes pour l'interprétation de données visuelles). Sorbonne University, CNRS, Paris, France, 2018 - [c3]Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord:
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection. NeurIPS 2018: 1317-1329 - 2017
- [c2]Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord:
Deformable Part-based Fully Convolutional Network for Object Detection. BMVC 2017 - [c1]Thibaut Durand, Taylor Mordan, Nicolas Thome, Matthieu Cord:
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation. CVPR 2017: 5957-5966 - [i1]Taylor Mordan, Nicolas Thome, Matthieu Cord, Gilles Hénaff:
Deformable Part-based Fully Convolutional Network for Object Detection. CoRR abs/1707.06175 (2017)
Coauthor Index
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