English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper explores the capacity of computer vision models to discerntemporal information in visual content, focusing specifically on historicalphotographs. We investigate the dating of images using OpenCLIP, an open-sourceimplementation of CLIP, a multi-modal language and vision model. Our experimentconsists of three steps: zero-shot classification, fine-tuning, and analysis ofvisual content. We use the \textit{De Boer Scene Detection} dataset, containing39,866 gray-scale historical press photographs from 1950 to 1999. The resultsshow that zero-shot classification is relatively ineffective for image dating,with a bias towards predicting dates in the past. Fine-tuning OpenCLIP with alogistic classifier improves performance and eliminates the bias. Additionally,our analysis reveals that images featuring buses, cars, cats, dogs, and peopleare more accurately dated, suggesting the presence of temporal markers. Thestudy highlights the potential of machine learning models like OpenCLIP indating images and emphasizes the importance of fine-tuning for accuratetemporal analysis. Future research should explore the application of thesefindings to color photographs and diverse datasets.

Blind Dates: Examining the Expression of Temporality in Historical Photographs