This book constitutes the refereed proceedings of the International Conference, VISIGRAPP 2012, the Joint Conference on Computer Vision Theory and Applications (VISAPP), on Computer Graphics Theory and Applications (GRAPP), and on Information Visualization Theory and Applications (IVAPP), held in Rome, Italy, in February 2012. The 28 revised full papers presented together with one invited paper were carefully reviewed and selected from 483 submissions. The papers are organized in topical sections on computer graphics theory and applications; information visualization theory and applications; computer vision theory and applications.

This book constitutes the refereed proceedings of the International Conference, VISIGRAPP 2011, the Joint Conference on Computer Vision, Theory and Applications (VISAPP), on Imaging Theory and Applications (IMAGAPP), on Computer Graphics Theory and Applications (GRAPP), and on Information Visualization Theory and Applications (IVAPP), held in Vilamoura, Portugal, in March 2011. The 15 revised full papers presented together with one invited paper were carefully reviewed and selected. The papers are organized in topical sections on computer graphics theory and applications; imaging theory and applications; information visualization theory and applications; and computer vision theory and applications.

Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.

Semantic image segmentation (SiS) plays a fundamental role towards a general understanding of the image content and context, in a broad variety of computer vision applications, thus providing key information for the global understanding of an image. This monograph summarizes two decades of research in the field of SiS, where a literature review of solutions starting from early historical methods is proposed, followed by an overview of more recent deep learning methods, including the latest trend of using transformers. The publication is complemented by presenting particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation, such as curriculum, incremental or self-supervised learning. State-of-the-art SiS models rely on a large amount of annotated samples, which are more expensive to obtain than labels for tasks such as image classification. Since unlabeled data is significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community. Therefore, a second core contribution of this monograph is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS), which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. In addition to providing a comprehensive survey on DASiS techniques, newer trends such as multi-domain learning, domain generalization, domain incremental learning, test-time adaptation and source-free domain adaptation are also presented. The publication concludes by describing datasets and benchmarks most widely used in SiS and DASiS and briefly discusses related tasks such as instance and panoptic image segmentation, as well as applications such as medical image segmentation. This monograph should provide researchers across academia and industry with a comprehensive reference guide, and will help them in fostering new research directions in the field.