  Olfaction, often overlooked in cultural heritage studies, holds profound
significance in shaping human experiences and identities. Examining historical
depictions of olfactory scenes can offer valuable insights into the role of
smells in history. We show that a transfer-learning approach using weakly
labeled training data can remarkably improve the classification of fragrant
spaces and, more generally, artistic scene depictions. We fine-tune
Places365-pre-trained models by querying two cultural heritage data sources and
using the search terms as supervision signal. The models are evaluated on two
manually corrected test splits. This work lays a foundation for further
exploration of fragrant spaces recognition and artistic scene classification.
All images and labels are released as the ArtPlaces dataset at
https://zenodo.org/doi/10.5281/zenodo.11584328.
