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2019 | OriginalPaper | Buchkapitel

Personalised Aesthetics with Residual Adapters

verfasst von : Carlos Rodríguez-Pardo, Hakan Bilen

Erschienen in: Pattern Recognition and Image Analysis

Verlag: Springer International Publishing

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Abstract

The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user-specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.

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Vorheriges Kapitel Non-destructively Prediction of Quality Parameters of Dry-Cured Iberian Ham by Applying Computer Vision and Low-Field MRI

Nächstes Kapitel An Improvement for Capsule Networks Using Depthwise Separable Convolution

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Titel: Personalised Aesthetics with Residual Adapters
verfasst von: Carlos Rodríguez-Pardo
Hakan Bilen
Verlag: Springer International Publishing
Buch: Pattern Recognition and Image Analysis
Print ISBN: 978-3-030-31331-9

Electronic ISBN: 978-3-030-31332-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-31332-6_44

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