Learning to Taste: A Multimodal Wine Dataset
Thoranna Bender, Simon Moe Sørensen, Alireza Kashani, Kristjan Eldjarn Hjorleifsson, Grethe Hyldig, Søren Hauberg, Serge Belongie, Frederik Rahbæk Warburg
Abstract
We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor. The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique bottlings, annotated with year, region, rating, alcohol percentage, price, and grape composition. We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances. We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels. We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and representing human perception of flavor.
BibTeX
@inproceedings{
bender2023learning,
title={Learning to Taste: A Multimodal Wine Dataset},
author={Thoranna Bender and Simon Moe S{\o}rensen and Alireza Kashani and Kristjan Eldjarn Hjorleifsson and Grethe Hyldig and S{\o}ren Hauberg and Serge Belongie and Frederik Rahb{\ae}k Warburg},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=VeJgZYhT7H}
}