activity
20172022
most citedMulti-task head pose estimation in-the-wild

87 citations · 128 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20226 cited

Shape Preserving Facial Landmarks with Graph Attention Networks

Andrés Prados-Torreblanca, José M. Buenaposada, Luis Baumela

Top-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance. However, it…

cs.CV202287 cited

Multi-task head pose estimation in-the-wild

Roberto Valle, José Miguel Buenaposada, Luis Baumela

We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dep…

cs.CV2021

Revisiting Binary Local Image Description for Resource Limited Devices

Iago Suárez, José M. Buenaposada, Luis Baumela

The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational r…

cs.CV2019

Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees

Roberto Valle, José M. Buenaposada, Antonio Valdés +1

Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the pr…

cs.CV201735 cited

Learning Depth from Monocular Videos using Direct Methods

Chaoyang Wang, Jose Miguel Buenaposada, Rui Zhu +1

The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are partic…