  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 \textit{De 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.
