Personalised aesthetics with residual adapters

Autor:	Rodríguez-Pardo, Carlos, Bilen, Hakan
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Statistics - Machine Learning 68T10 (Primary) 68T45 (Secondary) I.2.10 I.5.4 I.4.3
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-31332-6_44
Popis:	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. Comment: 12 pages, 4 figures. In Iberian Conference on Pattern Recognition and Image Analysis proceedings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.03802 Zobrazit plný text záznamu View this record from Arxiv