most citedFusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20231 cited

REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge

Siyang Song, Micol Spitale, Cheng Luo +8

The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learnin…

cs.CV20173 cited

Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation

Joy Egede, Michel Valstar, Brais Martinez

Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep lea…

cs.CV20161 cited

Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data

Shashank Jaiswal, Michel Valstar, Alinda Gillott +1

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Cu…

cs.CV2016

A CNN Cascade for Landmark Guided Semantic Part Segmentation

Aaron Jackson, Michel Valstar, Georgios Tzimiropoulos

This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of…

cs.CV2016

Cascaded Continuous Regression for Real-time Incremental Face Tracking

Enrique Sánchez-Lozano, Brais Martinez, Georgios Tzimiropoulos +1

This paper introduces a novel real-time algorithm for facial landmark tracking. Compared to detection, tracking has both additional challenges and opportunities. Arguably the most…