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Description:
Scene understanding, which aims to understand visual scenes comprehensively, stands as a pivotal element within the field of computer vision. To empower machine with the human-like scene understanding ability, semantic segmentation emerges as a crucial tool, forming the essence of a broad range of applications, e.g., autonomous driving, robot vision and human-computer interaction. Over the past decade, semantic segmentation models have achieved significant success, propelled by the availability of large-scale datasets and the rapid advancement of deep learning techniques. However, generalization of these models to new and different domains remains limited. Training domain-robust models typically relies on the labor-intensive process of labeling extensive and diverse datasets, resulting in significant costs and hindering the practical deployment of these models in real-world applications. In such cases, domain adaptation aims at adapting the semantic segmentation model trained on the labeled source domain to the unlabeled target domain, thereby eliminating the need for labeling the target domain. Traditional domain adaptation typically relies on implicit or explicit assumptions, such as assuming a single data distribution for the source or target domain, or maintaining consistent taxonomies between them. However, these assumptions prove impractical in real-world applications. Moreover, prevailing domain adaptation frameworks depend on pseudo-labels assigned to the unlabeled target domain, introducing noise due to domain discrepancies. The presence of low-quality pseudo-labels inevitably impedes the adaptation process. To tackle these challenges, this dissertation introduces a set of domain adaptive semantic segmentation methods that tackle these challenges and close to practical scenarios, ultimately enhancing scene understanding. We propose four main contributions detailed below. Firstly, we propose a multi-source domain adaptation and label unification (mDALU) problem along with a novel method to address it. In ...