CA 2.x - Christiche Archäologie im Inverted classroom. Ein Beitrag zur videobasierten digitalen Lehre an der Friedrich-Alexander-Universität Erlangen-Nürnberg - Verstegen, Ute; Mührenberg, Lara (Inverted Classroom - Past, Present & Future. Kompetenzorientiertes Lehren und Lernen im 21. Jahrhundert, 2020)

CA 2.x - Christliche Archäologie im Inverted Classroom - Verstegen, Ute; Mührenberg, Lara (Vom Untergrund ins Internet. Die Institute für Ur- und Frühgeschichte, Klassische Archäologie und Christliche Archäologie mit dem gemeinsamen Studiengang „Archäologische Wissenschaften“ an der Friedrich-Alexander-Universität Erlangen-Nürnberg, 2019)

Kann eine Künstliche Intelligenz antike Kunst verstehen? - Mührenberg, Lara; Bendschus, Torsten; Reinhardt, Corinna; Verstegen, Ute (Antike Welt : Zeitschrift für Archäologie und Kulturgeschichte, 2022)
Keywords: KI; AI; Archäologie; Computer Vision; Christliche Archäologie; Klassische Archäologie; Mustererkennung

One-Shot Object Detection in Heterogeneous Artwork Datasets - Madhu, Prathmesh; Meyer, Anna; Zinnen, Mathias; Mührenberg, Lara; Suckow, Dirk; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Verstegen, Ute; Kosti, Ronak Vijaypal; Maier, Andreas; Christlein, Vincent (2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2022)
Christian archeologists face many challenges in understanding visual narration through artwork images. This understanding is essential to access underlying semantic in-formation. Therefore, narrative elements (objects) need to be labeled, compared, and contextualized by experts, which takes an enormous amount of time and effort. Our work aims to reduce labeling costs by using one-shot object detection to generate a labeled database from unannotated images. Novel object categories can be defined broadly and annotated using visual examples of narrative elements without training exclusively for such objects. In this work, we propose two ways of using contextual information as data augmentation to improve the detection performance. Furthermore, we introduce a multi-relation detector to our framework, which extracts global, local, and patch-based relations of the image. Additionally, we evaluate the use of contrastive learning. We use data from Christian archeology (CHA) and art history - IconArt-v2 (IA). Our context encoding approach improves the typical fine-tuning approach in terms of mean average precision (mAP) by about 3.5 % (4 %) at 0.25 intersection over union (IoU) for UnSeen categories, and 6 % (1.5 %) for Seen categories in CHA (IA). To the best of our knowledge, our work is the first to explore few shot object detection on heterogeneous artistic data by investigating evaluation methods and data augmentation strategies. We will release the code and models after acceptance of the wor
Keywords: one-shot; object detection; digital humanities; computational humanities; data augmentation

Recognizing Characters in Art History Using Deep Learning - Madhu, Prathmesh; Kosti, Ronak Vijaypal; Mührenberg, Lara; Bell, Peter; Maier, Andreas; Christlein, Vincent (Recognizing Characters in Art History Using Deep Learning, 2019)
In the field of Art History, images of artworks and their contexts are core to understanding the underlying semantic information. However, the highly complex and sophisticated representation of these artworks makes it difficult, even for the experts, to analyze the scene. From the computer vision perspective, the task of analyzing such artworks can be divided into sub-problems by taking a bottom-up approach. In this paper, we focus on the problem of recognizing the characters in Art History. From the iconography of Annunciation of the Lord (Figure 1), we consider the representation of the main protagonists, Mary and Gabriel, across different artworks and styles. We investigate and present the findings of training a character classifier on features extracted from their face images. The limitations of this method, and the inherent ambiguity in the representation of Gabriel, motivated us to consider their bodies (a bigger context) to analyze in order to recognize the characters. Convolutional Neural Networks (CNN) trained on the bodies of Mary and Gabriel are able to learn person related features and ultimately improve the performance of character recognition. We introduce a new technique that generates more data with similar styles, effectively creating data in the similar domain. We present experiments and analysis on three different models and show that the model trained on domain related data gives the best performance for recognizing character. Additionally, we analyze the localized image regions for the network predictions.<br
Keywords: Recognizing Characters; Machine Learning; Convolutional Neural Networks