1.5k citations · 2.7k across the 37 of their papers we have counts for
7 papers · 1 filter
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…
High-Capacity Expert Binary Networks
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between…
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…
Training Binary Neural Networks with Real-to-Binary Convolutions
Brais Martinez, Jing Yang, Adrian Bulat +1
This paper shows how to train binary networks to within a few percent points () of the full precision counterpart. We first show how to build a strong baseline, which…
Knowledge distillation via adaptive instance normalization
Jing Yang, Brais Martinez, Adrian Bulat +1
This paper addresses the problem of model compression via knowledge distillation. To this end, we propose a new knowledge distillation method based on transferring feature statisti…
BATS: Binary ArchitecTure Search
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
This paper proposes Binary ArchitecTure Search (BATS), a framework that drastically reduces the accuracy gap between binary neural networks and their real-valued counterparts by me…
Toward fast and accurate human pose estimation via soft-gated skip connections
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1
This paper is on highly accurate and highly efficient human pose estimation. Recent works based on Fully Convolutional Networks (FCNs) have demonstrated excellent results for this…