1 research outputs found
Learning to adapt class-specific features across domains for semantic segmentation
Recent advances in unsupervised domain adaptation have shown the
effectiveness of adversarial training to adapt features across domains,
endowing neural networks with the capability of being tested on a target domain
without requiring any training annotations in this domain. The great majority
of existing domain adaptation models rely on image translation networks, which
often contain a huge amount of domain-specific parameters. Additionally, the
feature adaptation step often happens globally, at a coarse level, hindering
its applicability to tasks such as semantic segmentation, where details are of
crucial importance to provide sharp results. In this thesis, we present a novel
architecture, which learns to adapt features across domains by taking into
account per class information. To that aim, we design a conditional pixel-wise
discriminator network, whose output is conditioned on the segmentation masks.
Moreover, following recent advances in image translation, we adopt the recently
introduced StarGAN architecture as image translation backbone, since it is able
to perform translations across multiple domains by means of a single generator
network. Preliminary results on a segmentation task designed to assess the
effectiveness of the proposed approach highlight the potential of the model,
improving upon strong baselines and alternative designs.Comment: Master thesis dissertation for the Master in Computer Vision
(Barcelona). 11 pages main article and 3 pages appendices. Code availabl