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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.16900 |
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| _version_ | 1866917566967971840 |
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| author | Bender, Thoranna Sørensen, Simon Moe Kashani, Alireza Hjorleifsson, K. Eldjarn Hyldig, Grethe Hauberg, Søren Belongie, Serge Warburg, Frederik |
| author_facet | Bender, Thoranna Sørensen, Simon Moe Kashani, Alireza Hjorleifsson, K. Eldjarn Hyldig, Grethe Hauberg, Søren Belongie, Serge Warburg, Frederik |
| contents | 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 aligns with the intricate human perception of flavor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_16900 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning to Taste: A Multimodal Wine Dataset Bender, Thoranna Sørensen, Simon Moe Kashani, Alireza Hjorleifsson, K. Eldjarn Hyldig, Grethe Hauberg, Søren Belongie, Serge Warburg, Frederik Machine Learning 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 aligns with the intricate human perception of flavor. |
| title | Learning to Taste: A Multimodal Wine Dataset |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2308.16900 |