Self-Supervised Learning of Image Scale and Orientation. (arXiv:2206.07259v1 [cs.CV])

Web: http://arxiv.org/abs/2206.07259

We study the problem of learning to assign a characteristic pose, i.e., scale
and orientation, for an image region of interest. Despite its apparent
simplicity, the problem is non-trivial; it is hard to obtain a large-scale set
of image regions with explicit pose annotations that a model directly learns
from. To tackle the issue, we propose a self-supervised learning framework with
a histogram alignment technique. It generates pairs of image patches by random
rescaling/rotating and then train an estimator to …

arxiv cv image learning scale self-supervised learning supervised learning