# Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces

arXiv: https://arxiv.org/abs/2407.11701 · HTML: https://mlsanity.com/paper/2407.11701/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [WaveMix: A Resource-efficient Neural Network for Image Analysis](https://mlsanity.com/paper/2205.14375/) (2022)
- [ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition](https://mlsanity.com/paper/2301.09878/) (2023)
- [SniffyArt: The Dataset of Smelling Persons](https://mlsanity.com/paper/2311.11888/) (2023)
- [Transfer Learning for Olfactory Object Detection](https://mlsanity.com/paper/2301.09906/) (2023)
- [Smell and Emotion: Recognising emotions in smell-related artworks](https://mlsanity.com/paper/2407.04592/) (2024)

## Built on by (most cited first, 2 of 2)

- [Data Augmentation via Latent Diffusion Models for Detecting Smell-Related Objects in Historical Artworks](https://mlsanity.com/paper/2509.14755/) (2025)
- [Automated Goldsmith's Mark Retrieval in Silverware](https://mlsanity.com/paper/2609.20509/) (2026)

## Abstract (arXiv)

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.