Abstract:
This material presents the solution for shadow removal task using generative
adversarial networks. Our approach is trained in unsupervised fashion which means it
does not depend on time-consuming data collection and annotation. This together
with training in a single end-to-end framework significantly raises its practical
relevance.
Taking the existing method for unsupervised image transfer between different
domains we researched its applicability to the shadow removal problem. By
exploiting attention modules and multi context feature aggregation using dilated
convolutions our method gives significant results compared to existing solutions in
the field.
