  Dense regression is a widely used approach in computer vision for tasks such
as image super-resolution, enhancement, depth estimation, etc. However, the
high cost of annotation and labeling makes it challenging to achieve accurate
results. We propose incorporating active learning into dense regression models
to address this problem. Active learning allows models to select the most
informative samples for labeling, reducing the overall annotation cost while
improving performance. Despite its potential, active learning has not been
widely explored in high-dimensional computer vision regression tasks like
super-resolution. We address this research gap and propose a new framework
called USIM-DAL that leverages the statistical properties of colour images to
learn informative priors using probabilistic deep neural networks that model
the heteroscedastic predictive distribution allowing uncertainty
quantification. Moreover, the aleatoric uncertainty from the network serves as
a proxy for error that is used for active learning. Our experiments on a wide
variety of datasets spanning applications in natural images (visual genome,
BSD100), medical imaging (histopathology slides), and remote sensing (satellite
images) demonstrate the efficacy of the newly proposed USIM-DAL and superiority
over several dense regression active learning methods.
