@proceedings {7038,
	title = {Das Objekt jenseits der Digitalisierung},
	volume = {7},
	year = {2020},
	abstract = {Der technische Fortschritt der letzten Jahrzehnte hat disruptive Ver{\"a}nderungen f{\"u}r Gesellschaft, Wirtschaft und Wissenschaft gebracht: Die Digitalisierung ist ein Resultat dessen und beeinflusst, wie wir auf Daten zugreifen, diese verarbeiten, analysieren und Ergebnisse verbreiten. Obwohl dadurch bereits ein Wandel eingeleitet worden ist, kann das Digitalisieren von Textdokumenten oder Bildern nicht das endg{\"u}ltige Ziel sein. Der Fokus aktueller Bestrebungen sollte vielmehr auf der M{\"o}glichkeit der Weiterverarbeitung von Digitalisaten liegen {\textendash} dies schlie{\ss}t eine intelligente Informationsverarbeitung ein. Der Wert der Digitalisierung besteht nicht in der blo{\ss}en Anh{\"a}ufung digitaler Sammlun- gen, sondern in der Tatsache, dass sie weitaus mehr erm{\"o}glicht als das Analoge und daf{\"u}r die notwendigen Grundvoraussetzungen schafft.
Die Problematik besteht nun darin, dass die meisten Verarbeitungs- und Analyse- methoden f{\"u}r digitale Daten noch analog oder diesen nachempfunden sind: So werden digitale Sammlungen und darin enthaltene Bilder h{\"a}ufig noch mit den eigenen Augen, in traditionell komparativer Weise betrachtet und evaluiert. Dass dies aufgrund der F{\"u}lle an Daten nicht effizient ist, muss an dieser Stelle nicht betont werden. Obwohl das ana- loge und das digitale Bild den gleichen Inhalt zeigen k{\"o}nnen, haben beide doch ganz unterschiedliche Substrate. Ein Unterschied besteht zum Beispiel darin, dass digitale Bilder im Gegensatz zu analogen einfach manipuliert und dupliziert werden k{\"o}nnen. Das Digitale ist nicht das Analoge in neuer Form, und so bedarf es genuin digitaler Methoden f{\"u}r die Verarbeitung digitaler Daten. Durch die Entwicklung computerge- st{\"u}tzter Verfahren entstehen neue M{\"o}glichkeiten, Inhalte zu erschlie{\ss}en: Dazu geh{\"o}ren Ans{\"a}tze zur Objektsuche oder das Gruppieren und Sortieren der Daten entsprechend benutzerdefinierter Dimensionen; dies schlie{\ss}t {\"u}bergeordnete Kategorien wie Stil oder Genre, aber auch nuancierte Begriffe wie Alter oder Gewichtung der Bildkomposition ein. Doch das Digitale und entsprechende Verfahren k{\"o}nnen noch weitaus mehr leisten: Generative Verfahren, wie die Bildsynthese und Stilisierung eines Bildes, erm{\"o}glichen eine Blick{\"a}nderung auf das Artefakt und schlie{\ss}lich die Modifizierung des Objekts selbst. Wie h{\"a}tte ein K{\"u}nstler eine uns sichtbare Szene gemalt und dargestellt? Und wie sieht ein Mensch in der Pose eines anderen aus? Dies sind Fragen, die durch die Anwendung com- putergest{\"u}tzter Methoden beantwortet werden k{\"o}nnen. F{\"u}r das Museum haben diese Ans{\"a}tze eine besondere Relevanz, da sie neue Arten des Betrachtens und Vermittelns von Kunstwerken oder zum Beispiel die Rekonstruktion verlorener Artefakte erlauben. In Zusammenarbeit von Mensch und Maschine entstehen so neue effektive Verfahren, die Inhalte erschlie{\ss}en, Verbindungen etablieren und neues Wissen generieren.},
	isbn = {978-3-948808-00-6},
	url = {http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdf},
	author = {Sabine Lang and Bj{\"o}rn Ommer}
}
@article {7029,
	title = {Deep learning of cuneiform sign detection with weak supervision using transliteration alignment},
	journal = {PLoS ONE},
	volume = {15},
	year = {2020},
	month = {12/2020},
	type = {Journal},
	chapter = {1-21},
	abstract = {The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets. Deep learning requires large amounts of training data in the form of bounding boxes around cuneiform signs, which are not readily available and costly to obtain in the case of cuneiform script. To tackle this problem, we make use of existing transliterations, a sign-by-sign representation of the tablet content in Latin script. Since these do not provide sign localization, we propose a weakly supervised approach: We align tablet images with their corresponding transliterations to localize the transliterated signs in the tablet image, before using these localized signs in place of annotations to re-train the sign detector. A better sign detector in turn boosts the quality of the alignments. We combine these steps in an iterative process that enables training a cuneiform sign detector from transliterations only. While our method works weakly supervised, a small number of annotations further boost the performance of the cuneiform sign detector which we evaluate on a large collection of clay tablets from the Neo-Assyrian period. To enable experts to directly apply the sign detector in their study of cuneiform texts, we additionally provide a web application for the analysis of clay tablets with a trained cuneiform sign detector.},
	keywords = {cuneiform script, deep learning, sign detection, Weakly supervised learning},
	doi = {https://doi.org/10.1371/journal.pone.0243039},
	url = {https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform},
	author = {Tobias Dencker and Pablo Klinkisch and Stefan M Maul and Bj{\"o}rn Ommer}
}
@proceedings {6932,
	title = {A Disentangling Invertible Interpretation Network for Explaining Latent Representations},
	year = {2020},
	abstract = {Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.},
	url = {https://compvis.github.io/iin/},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
@proceedings {6934,
	title = {DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning},
	year = {2020},
	url = {https://arxiv.org/abs/2004.13458},
	author = {Timo Milbich and Karsten Roth and Homanga Bharadhwaj and Samarth Sinha and Yoshua Bengio and Bj{\"o}rn Ommer and Joseph Paul Cohen}
}
@proceedings {6299,
	title = {Divide and Conquer the Embedding Space for Metric Learning},
	year = {2019},
	keywords = {deep learning, metric learning},
	url = {https://github.com/CompVis/metric-learning-divide-and-conquer},
	author = {Sanakoyeu, A. and Tschernezki, V. and Uta B{\"u}chler and Bj{\"o}rn Ommer}
}
@article {6229,
	title = {Deep Unsupervised Learning of Visual Similarities},
	journal = {Pattern Recognition},
	volume = {78},
	year = {2018},
	chapter = {331},
	abstract = {Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.},
	doi = {https://doi.org/10.1016/j.patcog.2018.01.036},
	url = {https://authors.elsevier.com/a/1WXUt77nKSb25 },
	author = {Sanakoyeu, A. and Miguel Bautista and Bj{\"o}rn Ommer}
}
@conference {6136,
	title = {Deep Semantic Feature Matching},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	author = {Nikolai Ufer and Bj{\"o}rn Ommer}
}
@conference {6200,
	title = {Deep Unsupervised Similarity Learning using Partially Ordered Sets},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	author = {Miguel Bautista and Sanakoyeu, A. and Bj{\"o}rn Ommer}
}
@inbook {6109,
	title = {Digital Connoisseur? How Computer Vision Supports Art History},
	booktitle = {Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro \& S. Albl (ed.)},
	year = {2016},
	publisher = {Artemide},
	organization = {Artemide},
	address = {Rome},
	author = {Bell, P. and Bj{\"o}rn Ommer}
}
@conference {schlecht:ICIP:2011,
	title = {Detecting Gestures in Medieval Images},
	booktitle = {Proceedings of the International Conference on Image Processing},
	year = {2011},
	pages = {1309--1312},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Schlecht, J. and Bernd Carque and Bj{\"o}rn Ommer}
}
