no code implementations • 5 Sep 2022 • Shafa Balaram, Cuong M. Nguyen, Ashraf Kassim, Pavitra Krishnaswamy
Deep learning approaches achieve state-of-the-art performance for classifying radiology images, but rely on large labelled datasets that require resource-intensive annotation by specialists.
no code implementations • ICCV 2017 • Shengtao Xiao, Jiashi Feng, Luoqi Liu, Xuecheng Nie, Wei Wang, Shuicheng Yan, Ashraf Kassim
To address these challenging issues, we introduce a novel recurrent 3D-2D dual learning model that alternatively performs 2D-based 3D face model refinement and 3D-to-2D projection based 2D landmark refinement to reliably reason about self-occluded landmarks, precisely capture the subtle landmark displacement and accurately detect landmarks even in presence of extremely large poses.