# Blind Dates: Examining the Expression of Temporality in Historical Photographs

arXiv: https://arxiv.org/abs/2310.06633 · HTML: https://mlsanity.com/paper/2310.06633/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [How Much Can CLIP Benefit Vision-and-Language Tasks?](https://mlsanity.com/paper/2107.06383/) (2021)
- [A Century of Portraits: A Visual Historical Record of American High School Yearbooks](https://mlsanity.com/paper/1511.02575/) (2015)
- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [What's in a Decade? Transforming Faces Through Time](https://mlsanity.com/paper/2210.06642/) (2022)
- [Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach](https://mlsanity.com/paper/2106.05618/) (2021)
- [An Analytics of Culture: Modeling Subjectivity, Scalability, Contextuality, and Temporality](https://mlsanity.com/paper/2211.07460/) (2022)

## Built on by (most cited first, 5 of 5)

- [Photo Dating by Facial Age Aggregation](https://mlsanity.com/paper/2511.05464/) (2025)
- [MoralCLIP: Contrastive Alignment of Vision-and-Language Representations with Moral Foundations Theory](https://mlsanity.com/paper/2506.05696/) (2025)
- [A Matter of Time: Revealing the Structure of Time in Vision-Language Models](https://mlsanity.com/paper/2510.19559/) (2025)
- [Composed Historical Image Retrieval by Modeling Temporal Representations](https://mlsanity.com/paper/2608.18694/) (2026)
- [Uncertainty-Aware Art-Historical Dating with Vision-Language Models](https://mlsanity.com/paper/2608.18984/) (2026)

## Abstract (arXiv)

This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. Our experiment consists of three steps: zero-shot classification, fine-tuning, and analysis of visual content. We use the textitDe Boer Scene Detection dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. The results show that zero-shot classification is relatively ineffective for image dating, with a bias towards predicting dates in the past. Fine-tuning OpenCLIP with a logistic classifier improves performance and eliminates the bias. Additionally, our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers. The study highlights the potential of machine learning models like OpenCLIP in dating images and emphasizes the importance of fine-tuning for accurate temporal analysis. Future research should explore the application of these findings to color photographs and diverse datasets.