@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}
}
@conference {6193,
	title = {Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
	year = {2017},
	abstract = {Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine temporal ordering and spatial placement estimation as auxiliary tasks for learning pose similarities in a Siamese convolutional network. Since the self-supervised sampling of both tasks from natural videos can result in ambiguous and incorrect training labels, our method employs a curriculum learning idea that starts training with the most reliable data samples and gradually increases the difficulty. To further refine the training process we mine repetitive poses in individual videos which provide reliable labels while removing inconsistencies. Our pose embeddings capture visual characteristics of human pose that can boost existing supervised representations in human pose estimation and retrieval. We report quantitative and qualitative results on these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.},
	author = {{\"O}mer S{\"u}mer and Tobias Dencker and Bj{\"o}rn Ommer}
}
