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We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA).
Defining computational aesthetics
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Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german
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Beauty and the beholder: Highly individual taste for abstract, but not real-world images
Edward A Vessel and Nava Rubin · 2010
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Ava: A large-scale database for aesthetic visual analysis
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Normalization is a general neural mechanism for context-dependent decision making
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Not just for consumers: Context effects are fundamental to decision making
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Jenaesthetics subjective dataset: analyzing paintings by subjective scores
Seyed Ali Amirshahi, Gregor Uwe Hayn-Leichsenring, Joachim Denzler, and Christoph Redies · 2015
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Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Babak Saleh and Ahmed Elgammal · 2015
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Photo aesthetics ranking network with attributes and content adaptation
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Personalized image aesthetic quality assessment by joint regression and ranking
Kayoung Park, Seunghoon Hong, Mooyeol Baek, and Bohyung Han · 2017
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Personalized image aesthetics
Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, and David J Foran · 2017
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Bam! the behance artistic media dataset for recognition beyond photography
Michael J Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, and Serge Belongie · 2017
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Usar: An interactive user-specific aesthetic ranking framework for images
Pei Lv, Meng Wang, Yongbo Xu, Ze Peng, Junyi Sun, Shimei Su, Bing Zhou, and Mingliang Xu · 2018
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Cross-platform and cross-interaction study of user personality based on images on twitter and flickr
Zahra Riahi Samani, Sharath Chandra Guntuku, Mohsen Ebrahimi Moghaddam, Daniel Preoţiuc-Pietro, and Lyle H Ungar · 2018
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Stronger shared taste for natural aesthetic domains than for artifacts of human culture
Edward A Vessel, Natalia Maurer, Alexander H Denker, and G Gabrielle Starr · 2018
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Collaborative and attentive learning for personalized image aesthetic assessment
Guolong Wang, Junchi Yan, and Zheng Qin · 2018
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Meta-learning perspective for personalized image aesthetics assessment
Weining Wang, Junjie Su, Lemin Li, Xiangmin Xu, and Jiebo Luo · 2019
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Eva: An explainable visual aesthetics dataset
Chen Kang, Giuseppe Valenzise, and Frédéric Dufaux · 2020
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Personality-assisted multi-task learning for generic and personalized image aesthetics assessment
Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, and Weisi Lin · 2020
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The impact of covid-19 on the gallery sector
Personalized image aesthetics assessment with rich attributes
Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, and Yandong Guo · 2022
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Personalized image aesthetics assessment via multi-attribute interactive reasoning
Hancheng Zhu, Yong Zhou, Zhiwen Shao, Wenliang Du, Guangcheng Wang, and Qiaoyue Li · 2022
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Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
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jspsych: Enabling an open-source collaborative ecosystem of behavioral experiments
Joshua R de Leeuw, Rebecca A Gilbert, and Björn Luchterhandt · 2023
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The vienna art picture system (vaps): A data set of 999 paintings and subjective ratings for art and aesthetics research
Anna Fekete, Matthew Pelowski, Eva Specker, David Brieber, Raphael Rosenberg, and Helmut Leder · 2023
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Clare McAndrew · 2020
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The vienna art interest and art knowledge questionnaire (vaiak): A unified and validated measure of art interest and art knowledge
Eva Specker, Michael Forster, Hanna Brinkmann, Jane Boddy, Matthew Pelowski, Raphael Rosenberg, and Helmut Leder · 2020
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Personalized image aesthetics assessment via meta-learning with bilevel gradient optimization
Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, and Guangming Shi · 2020
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Artemis: Affective language for visual art
Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, and Leonidas J Guibas · 2021
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Learning personalized image aesthetics from subjective and objective attributes
Hancheng Zhu, Yong Zhou, Leida Li, Yaqian Li, and Yandong Guo · 2021
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How rating scales influence responses’ reliability, extreme points, middle point and respondent’s preferences
Naia A de Rezende and Denise D de Medeiros · 2022
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Rethinking image aesthetics assessment: Models, datasets and benchmarks
Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, and Anlong Ming · 2022
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Correct for whom? subjectivity and the evaluation of personalized image aesthetics assessment models
Samuel Goree, Weslie Khoo, and David J Crandall · 2023
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Multi-level transitional contrast learning for personalized image aesthetics assessment
Zhichao Yang, Leida Li, Yuzhe Yang, Yaqian Li, and Weisi Lin · 2023
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Towards artistic image aesthetics assessment: a large-scale dataset and a new method
Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai, and Paul L Rosin · 2023
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Personalized image aesthetics assessment with attribute-guided fine-grained feature representation
Hancheng Zhu, Zhiwen Shao, Yong Zhou, Guangcheng Wang, Pengfei Chen, and Leida Li · 2023
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Personalized image aesthetics assessment based on graph neural network and collaborative filtering
Huiying Shi, Jing Guo, Yongzhen Ke, Kai Wang, Shuai Yang, Fan Qin, and Liming Chen · 2024
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Further validating the vaiak: Defining a psychometric model, configural measurement invariance, reliability, and practical guidelines
Eva Specker · 2024
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Backflip: The impact of local and global data augmentations on artistic image aesthetic assessment
Ombretta Strafforello, Gonzalo Muradas Odriozola, Fatemeh Behrad, Li-Wei Chen, Anne-Sofie Maerten, Derya Soydaner, and Johan Wagemans · 2024
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Hybrid cnn-transformer based meta-learning approach for personalized image aesthetics assessment
Xingao Yan, Feng Shao, Hangwei Chen, and Qiuping Jiang · 2024
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A toolbox for calculating quantitative image properties in aesthetics research
Christoph Redies, Ralf Bartho, Lisa Koßmann, Branka Spehar, Ronald Hübner, Johan Wagemans, and Gregor U Hayn-Leichsenring · 2025
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