4 accepted papers
"We introduce a novel non-parametric sensor noise model that directly constructs probability mass functions per intensity level from captured images. We show that our noise model provides a more accurate fit to real sensor noise than existing models. We detail the capture procedure for deriving our…
Computer graphics (CG) rendering platforms produce imagery with ever-increasing photo realism. The narrowing domain gap between real and synthetic imagery makes it possible to use CG images as training data for deep learning models targeting high-level computer vision tasks, such as autonomous drivi…
Over the past few years, image-to-image style transfer has risen to the frontiers of neural image processing. While conventional methods were successful in various tasks such as color and texture transfer between images, none could effectively work with the custom filter effects that are applied by…
Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images. To address such severe matching ambiguities, we introduce a novel approach, called {guided semantic flow}, based on the key insight that sparse yet reliable mat…