@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 {Hoai2014,
	title = {Learning discriminative localization from weakly labeled data},
	booktitle = {Pattern Recognition},
	volume = {47},
	number = {3},
	year = {2014},
	month = {mar},
	pages = {1523{\textendash}1534},
	abstract = {Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g., in terms of window size and location) which may be suboptimal for classification. We propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. {\textcopyright} 2013 Elsevier Ltd. All rights reserved.},
	keywords = {Discriminative discovery, Event detection, Image classification, Object detection, Time series classification, Weakly supervised learning},
	issn = {00313203},
	doi = {10.1016/j.patcog.2013.09.028},
	author = {Hoai, Minh and Torresani, Lorenzo and De La Torre, Fernando and Carsten Rother}
}
