Taylor Mordan

According to our database, Taylor Mordan authored at least 14 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Toward Reliable Human Pose Forecasting with Uncertainty.

Saeed Saadatnejad, Mehrshad Mirmohammadi, Matin Daghyani, Parham Saremi, Yashar Zoroofchi Benisi, Amirhossein Alimohammadi, Zahra Tehraninasab, Taylor Mordan, Alexandre Alahi

CoRR, 2023

A generic diffusion-based approach for 3D human pose prediction in the wild.

Saeed Saadatnejad, Ali Rasekh, Mohammadreza Mofayezi, Yasamin Medghalchi, Sara Rajabzadeh, Taylor Mordan, Alexandre Alahi

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022

A Shared Representation for Photorealistic Driving Simulators.

Saeed Saadatnejad, Siyuan Li, Taylor Mordan, Alexandre Alahi

IEEE Trans. Intell. Transp. Syst., 2022

Detecting 32 Pedestrian Attributes for Autonomous Vehicles.

Taylor Mordan, Matthieu Cord, Patrick Pérez, Alexandre Alahi

IEEE Trans. Intell. Transp. Syst., 2022

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion.

Dongxu Guo, Taylor Mordan, Alexandre Alahi

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021

Do Pedestrians Pay Attention? Eye Contact Detection in the Wild.

Younes Belkada, Lorenzo Bertoni, Romain Caristan, Taylor Mordan, Alexandre Alahi

CoRR, 2021

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization.

Lorenzo Bertoni, Sven Kreiss, Taylor Mordan, Alexandre Alahi

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Learning Decoupled Representations for Human Pose Forecasting.

Behnam Parsaeifard, Saeed Saadatnejad, Yuejiang Liu, Taylor Mordan, Alexandre Alahi

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

2019

End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection.

Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord

Int. J. Comput. Vis., 2019

2018

Designing Deep Architectures for Visual Understanding. (Conception d'architectures profondes pour l'interprétation de données visuelles).

Taylor Mordan

PhD thesis, 2018

Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection.

Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation.

Thibaut Durand, Taylor Mordan, Nicolas Thome, Matthieu Cord

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Deformable Part-based Fully Convolutional Network for Object Detection.

Taylor Mordan, Nicolas Thome, Gilles Hénaff, Matthieu Cord

Proceedings of the British Machine Vision Conference 2017, 2017