# Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach

arXiv: https://arxiv.org/abs/2106.05618 · HTML: https://mlsanity.com/paper/2106.05618/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval](https://mlsanity.com/paper/2007.12163/) (2020)
- [A Century of Portraits: A Visual Historical Record of American High School Yearbooks](https://mlsanity.com/paper/1511.02575/) (2015)
- [When was that made?](https://mlsanity.com/paper/1608.03914/) (2016)

## Built on by (most cited first, 4 of 4)

- [Blind Dates: Examining the Expression of Temporality in Historical Photographs](https://mlsanity.com/paper/2310.06633/) (2023)
- [Photo Dating by Facial Age Aggregation](https://mlsanity.com/paper/2511.05464/) (2025)
- [A Generic Image Retrieval Method for Date Estimation of Historical Document Collections](https://mlsanity.com/paper/2204.04028/) (2022)
- [Uncertainty-Aware Art-Historical Dating with Vision-Language Models](https://mlsanity.com/paper/2608.18984/) (2026)

## Abstract (arXiv)

This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.