  This work investigates how hierarchically structured data can help neural
networks learn conceptual representations of cathedrals. The underlying
WikiScenes dataset provides a spatially organized hierarchical structure of
cathedral components. We propose a novel hierarchical contrastive training
approach that leverages a triplet margin loss to represent the data's spatial
hierarchy in the encoder's latent space. As such, the proposed approach
investigates if the dataset structure provides valuable information for
self-supervised learning. We apply t-SNE to visualize the resultant latent
space and evaluate the proposed approach by comparing it with other
dataset-specific contrastive learning methods using a common downstream
classification task. The proposed method outperforms the comparable
weakly-supervised and baseline methods. Our findings suggest that dataset
structure is a valuable modality for weakly-supervised learning.
