This paper demonstrates how to use generative models trained for image synthesis as tools
for visual data mining. Our insight is that since contemporary generative models learn an …

Progress in the digitization of cultural assets leads to online databases that become too
large for a human to analyze. Moreover, some analyses might be challenging, even for …

Watermarks in historical manuscripts are figural shapes serving as tokens for provenance
research (eg scribe identification, dating, papermill attribution, scribe-papermaker relation …

Historical watermark identification is of paramount importance to archivists and historians for
undertaking studies on historical documents. The identification problem is usually treated as …

The identification and restoration of ancient watermarks have long been a major topic in
codicology and history. Classifying historical documents based on watermarks can be …

Illustrations are an essential transmission instrument. For an historian, the first step in
studying their evolution in a corpus of similar manuscripts is to identify which ones …

In this paper, we tackle the problem of categorizing and identifying cross-depicted historical
motifs using recent deep learning techniques, with aim of developing a content-based image …

Archaeology is an intriguing domain for computer vision. It suffers not only from shortage in
(labeled) data, but also from highly-challenging data, which is often extremely abraded and …

This presentation will give an overview of projects on leveraging deep learning for historical
data analysis my group did in the last 3 years, partly in the context of the ANR EnHerit …

The search for specific objects or motifs is essential to art history as both assist in decoding
the meaning of artworks. Digitization has produced large art collections, but manual …