Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation
Donald Shenaj,
Francesco Barbato,
Umberto Michieli
et al.
Abstract:Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can change. The problem becomes even more challenging in dense predictive tasks, such as semantic segmentation, and furthermore most approaches tackle the two problems separately. In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift. We consider subsequent l… Show more
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