Multimodal Metadata Assignment for Cultural Heritage Artifacts
This addresses metadata assignment for digitized silk artifacts in the cultural heritage domain, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of predicting missing metadata for cultural heritage artifacts by developing a multimodal classifier using image, text, and tabular data, achieving the best results with a late fusion approach.
We develop a multimodal classifier for the cultural heritage domain using a late fusion approach and introduce a novel dataset. The three modalities are Image, Text, and Tabular data. We based the image classifier on a ResNet convolutional neural network architecture and the text classifier on a multilingual transformer architecture (XML-Roberta). Both are trained as multitask classifiers and use the focal loss to handle class imbalance. Tabular data and late fusion are handled by Gradient Tree Boosting. We also show how we leveraged specific data models and taxonomy in a Knowledge Graph to create the dataset and to store classification results. All individual classifiers accurately predict missing properties in the digitized silk artifacts, with the multimodal approach providing the best results.