Search Results for author:
Found 3 papers, 2 papers with code
Do Neural Networks for Segmentation Understand Insideness?
Only recurrent networks trained with small images learn solutions that generalize well to almost any curve.
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
Do Deep Neural Networks for Segmentation Understand Insideness?
At the heart of image segmentation lies the problem of determining whether a pixel is inside or outside a region, which we denote as the "insideness" problem.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
