Furthermore, in the last decade, deep learning approaches (e.g., Convolutional Neural Networks (CNN) and Generative Adversarial Network (GAN)) have achieved unprecedented performance in computer vision. Despite these results, research on deep learning techniques has so far mainly focused on Euclidean data.
In this talk, based on our recent results, I will show that putting together the geometry and deep learning can lead to elegant solutions to various problems in computer vision.
Lecturer: Mohamed Daoudi is a professor of Computer Science at IMT Lille Douai and a leader of Image group at CRIStAL (UMR CNRS 9189). His research interests include pattern recognition and computer vision and he is the author of over 150 scientific publications that have appeared in the most distinguished international journals and conference proceedings.
He is the editor of several books including “3D Face Modeling, Analysis and Recognition” Wiley 2013 and “3D Object Processing: Compression, Indexing and Watermarking” Wiley 2008. He is a Senior Member of IEEE and a Fellow of IAPR. He is an Associate editor of Image and Vision Computing Journal (since 2016), IEEE Transactions on Multimedia (since 2018) and Journal of Imaging (since 2018). He has been Guest Editor of several Special Issues on 3D Humans Analysis and Recognition, face and gesture recognition, and manifolds for computer vision on some of the most prestigious scientific journals.
He has also been a member of the Scientific Program Committee of many international conferences in the fields of computer vision and artificial intelligence. He has been General Chair of the 14th IEEE International Conference on Automatic Face and Gesture Recognition, IEEE FG 2019, and of the International Conference on Intelligent Systems and Computer Vision 2017, ISCV 2017, and of several other international workshops.
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