We stand with the broader scientific community both in the United States and internationally in our commitment to equity, diversity, and inclusion in the participation of this workshop. We have to continuously keep in mind that we are not doing enough and have to do more. In the light of recent events that have once again brought to light the historic systemic inequalities that have especially targeted black persons, we also refer our participants to the CVPR statement on equity, diversity, and inclusion, behind which we solidly stand in support.

DiffCVML 2020 will be held on Friday June 19'th and will be fully virtual. Attendees who have registered can go to the link DiffCVML20 at CVPR2020 to participate virtually in the workshop.

Workshop Theme

Traditional machine learning, pattern recognition and data analysis methods often assume that input data can be represented well by elements of Euclidean space. While this assumption has worked well for many past applications, researchers have increasingly realized that most data in vision and pattern recognition is intrinsically non-Euclidean, i.e. standard Euclidean calculus does not apply. The exploitation of this geometrical information can lead to more accurate representation the inherent structure of the data, better algorithms and better performance in practical applications. In particular, Riemannian geometric principles can be applied to a variety of difficult computer vision problems including face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion, to name a few. Consequently, Riemannian geometric computing has become increasingly popular in the computer vision community. Besides nice mathematical formulations, Riemannian computations based on the geometry of underlying manifolds are often faster and more stable than their classical, Euclidean counterparts. This workshop focuses on both advances in differential geometric methods for computer vision and medical imaging, as well as topics that exploit machine learning algorithms that efficiently perform inference on geometric structures from data.

General Topics

Deep learning and geometry. Riemannian methods in computer vision Statistical shape analysis: detection, estimation, and inference. Statistical analysis on manifolds Manifold-valued features and learning Machine learning on nonlinear manifolds Shape detection, tracking and retrieval. Topological methods in structure analysis Functional Data Analysis: Hilbert manifolds, Visualization. Applications: medical imaging and analysis, graphics, biometrics, activity recognition, bioinformatics, etc.