# The Role of Generative Systems in Historical Photography Management: A Case Study on Catalan Archives

arXiv: https://arxiv.org/abs/2409.03911 · HTML: https://mlsanity.com/paper/2409.03911/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 21 of 21)

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- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
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- [Generalized Data Augmentation for Low-Resource Translation](https://mlsanity.com/paper/1906.03785/) (2019)
- [Self-supervised learning of visual features through embedding images into text topic spaces](https://mlsanity.com/paper/1705.08631/) (2017)
- [Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph](https://mlsanity.com/paper/2107.11970/) (2021)
- [Show, Interpret and Tell: Entity-aware Contextualised Image Captioning in Wikipedia](https://mlsanity.com/paper/2209.10474/) (2022)
- [Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data](https://mlsanity.com/paper/2107.06777/) (2021)

## Abstract (arXiv)

The use of image analysis in automated photography management is an increasing trend in heritage institutions. Such tools alleviate the human cost associated with the manual and expensive annotation of new data sources while facilitating fast access to the citizenship through online indexes and search engines. However, available tagging and description tools are usually designed around modern photographs in English, neglecting historical corpora in minoritized languages, each of which exhibits intrinsic particularities. The primary objective of this research is to study the quantitative contribution of generative systems in the description of historical sources. This is done by contextualizing the task of captioning historical photographs from the Catalan archives as a case study. Our findings provide practitioners with tools and directions on transfer learning for captioning models based on visual adaptation and linguistic proximity.