When was that made?
This work addresses a domain-specific problem for historians, collectors, and individuals interested in artifact dating, with applications in data organization and fashion analysis.
The paper tackles the problem of estimating when objects were made using deep learning, achieving state-of-the-art performance by outperforming color-based baselines and previous methods on new datasets of dated clothing items.
In this paper, we explore deep learning methods for estimating when objects were made. Automatic methods for this task could potentially be useful for historians, collectors, or any individual interested in estimating when their artifact was created. Direct applications include large-scale data organization or retrieval. Toward this goal, we utilize features from existing deep networks and also fine-tune new networks for temporal estimation. In addition, we create two new datasets of 67,771 dated clothing items from Flickr and museum collections. Our method outperforms both a color-based baseline and previous state of the art methods for temporal estimation. We also provide several analyses of what our networks have learned, and demonstrate applications to identifying temporal inspiration in fashion collections.