Polynomial Neural Fields for Subband Decomposition and Manipulation
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Polynomial Neural Fields for Subband Decomposition and Manipulation. / Bender, Thoranna ; Sørensen, Simon Moe ; Kashani, Alireza; Hjorleifsson, K. Eldjarn; Hyldig, Grethe; Hauberg, Søren; Belongie, Serge; Warburg, Frederik .
Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023. Neural Information Processing Systems Foundation, 2023. (Advances in Neural Information Processing Systems, Vol. 36).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Polynomial Neural Fields for Subband Decomposition and Manipulation
AU - Bender, Thoranna
AU - Sørensen, Simon Moe
AU - Kashani, Alireza
AU - Hjorleifsson, K. Eldjarn
AU - Hyldig, Grethe
AU - Hauberg, Søren
AU - Belongie, Serge
AU - Warburg, Frederik
N1 - Publisher Copyright: © 2022 Neural information processing systems foundation. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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 vintages, 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.
AB - 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 vintages, 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.
M3 - Article in proceedings
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023
PB - Neural Information Processing Systems Foundation
T2 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
Y2 - 28 November 2022 through 9 December 2022
ER -
ID: 384576085