no code implementations • 4 Apr 2024 • Rui Wang, Chuanfu Shen, Manuel J. Marin-Jimenez, George Q. Huang, Shiqi Yu
Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments.
no code implementations • 29 Nov 2022 • Heeseung Kwon, Francisco M. Castro, Manuel J. Marin-Jimenez, Nicolas Guil, Karteek Alahari
Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators.
1 code implementation • 6 Jan 2021 • Manuel J. Marin-Jimenez, Vicky Kalogeiton, Pablo Medina-Suarez, Andrew Zisserman
For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos.
no code implementations • 31 Aug 2020 • Zihao Mu, Francisco M. Castro, Manuel J. Marin-Jimenez, Nicolas Guil, Yan-ran Li, Shiqi Yu
In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset.
1 code implementation • CVPR 2019 • Manuel J. Marin-Jimenez, Vicky Kalogeiton, Pablo Medina-Suarez, Andrew Zisserman
For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos.
1 code implementation • 14 Jul 2018 • Manuel J. Marin-Jimenez, Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, Rafael Medina-Carnicer
This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach.