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Vor dem Hintergrund der Themenfelder Heimat, Erinnerung und Identität fragt dieser Band danach, was eigentlich die Brandenburger zusammenhält und miteinander verbindet.
Bei den hier vorgestellten Erinnerungsorten handelt sich nicht allein um klar lokalisierbare Fixpunkte, sondern auch um Ereignisse, Personen, Brauchtümer oder Slogans. Auf symbolisch-emotionaler Ebene wird durch gemeinsames Erinnern Identität gestiftet, so dass eine brandenburgische Geschichtskultur entsteht, die über Generationen anhalten und sich wandeln kann
Brevitas et prolixitas
(2021)
Bürgerliches Recht
(2021)
Chinas neuer langer Marsch
(2021)
Come Together
(2021)
Como povo marítimo que no século XV iniciou a expansão atlântica europeia, os portugueses entraram cedo em contacto com muitas culturas e línguas diferentes. O contacto entre as línguas fez surgir variações, o que atualmente se traduz numa polifonia no espaço linguístico lusófono e em cujo desenvolvimento ocorreram vários processos de transformação.
Dante e Botticelli
(2021)
In recent years, computer vision algorithms based on machine learning have seen rapid development. In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes. Nowadays other fields such as the field of cultural heritage, where an abundance of data is available, also get into the focus of research. In the line of current research endeavours, we collaborated with the Getty Research Institute which provided us with a challenging dataset, containing images of paintings and drawings. In this technical report, we present the results of the seminar "Deep Learning for Computer Vision". In this seminar, students of the Hasso Plattner Institute evaluated state-of-the-art approaches for image classification, object detection and image recognition on the dataset of the Getty Research Institute. The main challenge when applying modern computer vision methods to the available data is the availability of annotated training data, as the dataset provided by the Getty Research Institute does not contain a sufficient amount of annotated samples for the training of deep neural networks. However, throughout the report we show that it is possible to achieve satisfying to very good results, when using further publicly available datasets, such as the WikiArt dataset, for the training of machine learning models.