Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature.
Application of deep neural networks to medical imaging tasks has in some sense become commonplace.
We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications.
That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target).
Such models may work for cross-sectional studies, however, they are not suitable to generate data for longitudinal studies that focus on "progressive" behavior in a sequence of data.
Visual relationships provide higher-level information of objects and their relations in an image â this enables a semantic understanding of the scene and helps downstream applications.
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make.
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.