activity
20182022
most citedObject landmark discovery through unsupervised adaptation

8 citations · 11 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2022

REST: REtrieve & Self-Train for generative action recognition

Adrian Bulat, Enrique Sanchez, Brais Martinez +1

This work is on training a generative action/video recognition model whose output is a free-form action-specific caption describing the video (rather than an action class label). A…

cs.CV20212 cited

Subpixel Heatmap Regression for Facial Landmark Localization

Adrian Bulat, Enrique Sanchez, Georgios Tzimiropoulos

Deep Learning models based on heatmap regression have revolutionized the task of facial landmark localization with existing models working robustly under large poses, non-uniform i…

cs.CV2021

Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition

Enrique Sanchez, Mani Kumar Tellamekala, Michel Valstar +1

Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the…

cs.CV2020

Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

Enrique Sanchez, Adrian Bulat, Anestis Zaganidis +1

This paper tackles the challenging problem of estimating the intensity of Facial Action Units with few labeled images. Contrary to previous works, our method does not require to ma…

cs.CV2020

A recurrent cycle consistency loss for progressive face-to-face synthesis

Enrique Sanchez, Michel Valstar

This paper addresses a major flaw of the cycle consistency loss when used to preserve the input appearance in the face-to-face synthesis domain. In particular, we show that the ima…

cs.CV20201 cited

A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation

Ioanna Ntinou, Enrique Sanchez, Adrian Bulat +2

Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations. Motivated by this observation we propose…