论文

2021

Hao Lou, Heng Huang，Chaoen Xiao and Xin Jin：Aesthetic Evaluation and Guidance for Mobile Photography. MM(2021) [pdf]
Pei Lv, Jianqi Fan, Xixi Nie, Weiming Dong. User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning. TMM(2021) [pdf]
Xin Jin, Zhonglan Li, Ke Liu, Dongqing Zou, Xiaodong Li, Xingfan Zhu, Ziyin Zhou, Qilong Sun, Qingyu Liu. Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies. MM(2021) [pdf]
Dongyu She, Yu-Kun Lai, Gaoxiong Yi, Kun Xu: “Hierarchical layout-Aware graph convolutional network for unified aesthetics assessment.” CVPR (2021) [pdf]
Jingwen Hou, Sheng Yang, Weisi Lin, Baoquan Zhao, and Yuming Fang. Learning Image Aesthetic Assessment from Object-level Visual Components. TIP(2021) [pdf]

2020

Jingwen Hou, Sheng Yang, Weisi Lin:“Object-level attention for aesthetic rating distribution prediction.” ACM MM (2020) [pdf]
Munan Xu, Jia-Xing Zhong, Yurui Ren. Context-aware Attention Network for Predicting Image Aesthetic Subjectivity. MM(2020) [pdf]
Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma: “Revisiting image aesthetic assessment via self-supervised feature learning.” AAAI (2020) [pdf]
Qiuyu Chen, Wei Zhang, Ning Zhou, Peng Lei, Yi Xu, Yu Zheng, Jianping Fan: “Adaptive fractional dilated convolution network for image aesthetics assessment.” CVPR (2020) [pdf]
Hui Zeng, Zisheng Cao, Lei Zhang, Alan C. Bovik: “A unified probabilistic formulation of image aesthetic assessment.” TIP (2020) [pdf] [code]
Dong Liu, Rohit Puri, Nagendra Kamath, Subhabrata Bhattacharya: “Composition-aware image aesthetics assessment.” WACV(2020) [pdf]
Qi Kuang , Xin Jin , Qinping Zhao, and Bin Zhou. Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment. TMM(2020) [pdf]
Lijie Wang, Xueting Wang, Toshihiko Yamasaki. Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents. arXiv(2020) [pdf]
Leida Li , Hancheng Zhu , Sicheng Zhao , Guiguang Ding ,and Weisi Lin. Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment. TIP(2020) [pdf]

2019

Weining Wang, Rui Deng: “Modeling human perception for image aesthetic assessme.” ICIP (2019) [pdf]
JUN-TAE LEE1, CHUL LEE, AND CHANG-SU KIM. Property-Specific Aesthetic Assessment With Unsupervised Aesthetic Property Discovery. Acess(2019) [pdf]
Vlad Hosu, Bastian GoldluModeling human perception for image aesthetic assessmen. multi-level spatially pooled features." CVPR (2019) [pdf] [code]
Jun-Tae Lee† and Chang-Su Kim. Image Aesthetic Assessment Based on Pairwise Comparison – A Unified Approach to Score Regression, Binary Classification, and Personalization. ICCV(2019) [pdf]
Xin Jin, Le Wu, Geng Zhao, Xiaodong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou: “Aesthetic attributes assessment of images.” ACM MM (2019) [pdf] [project]
Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, Hongyan Jiang, Allen Tan: “Personality driven multi-task learning for image aesthetic assessment.” ICME (2019) [pdf]
Ning Ma, Alexey Volkov, Aleksandr Livshits, Pawel Pietrusinski, Houdong Hu, Mark Bolin: “An universal image attractiveness ranking framework.” WACV (2019) [pdf]

2018

Jun-Tae Lee, Han-Ul Kim, Chul Lee, Chang-Su Kim: “Photographic composition classification and dominant geometric element detection for outdoor scenes.” JVCIR (2018) [pdf] [code]
Andrew Gilbert, John Collomosse, Hailin Jin, and Brian Price. Disentangling Structure and Aesthetics for Style-aware Image Completion. CVPR(2018) [pdf]
Xiaodan Zhang, Xinbo Gao, Wen Lu, and Lihuo He. A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction. TMM(2018) [pdf]
Katja Thömmes* and Ronald Hübner: “Instagram likes for architectural photos can be predicted by quantitative balance measures and curvature.” Front Psychol (2018) [pdf]
Kekai Sheng, Weiming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu: “Attention-based multi-patch aggregation for image aesthetic assessment.” ACM MM (2018) [pdf] [code]
Ning Yu, Xiaohui Shen, Zhe Lin, Radomir Mech, Connelly Barnes: “Learning to detect multiple photographic defects.” WACV (2018) [pdf]
Keunsoo Ko, Jun Tae Lee, Chang-Su Kim: “PAC-Net: Pairwise aesthetic comparison network for image aesthetic assessment.” ICIP (2018) [pdf]
Hossein Talebi and Peyman Milanfar: “NIMA: Neural image assessment.” TIP (2018) [pdf] [code]
Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch: “Will people like your image? Learning the aesthetic space.” WACV (2018) [pdf] [code]
Guolong Wang, Junchi Yan, Zheng Qin: “Collaborative and attentive learning for personalized image aesthetic assessment.” IJCAI (2018) [pdf]
Xin Jin, Le Wu, Xiaodong Li, Siyu Chen. Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence. AAAI(2018) [pdf]
Xin Jin, Le Wu, Xiaodong Li, Xiaokun Zhang. ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation. IET(2018) [pdf] [code]
Wenguan Wang, Jianbing Shen, and Haibin Ling. A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping. TPAMI(2018) [pdf]
Michal Kucer, David W. Messinger. Aesthetic Inference for Smart Mobile Devices. WACV(2018) [pdf]

2017

Shuang Ma, Jing Liu, Chang Wen Chen: “A-Lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment.” CVPR (2017) [pdf] [code]
Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, David J. Foran: “Personalized image aesthetics.” ICCV (2017) [pdf] [code]
Anselm Brachmann and Christoph Redies: “Computational and experimental approaches to visual aesthetics.” Front Hum Neurosci (2017) [pdf]
Naila Murray and Albert Gordo. A deep architecture for unified aesthetic prediction. arXiv(2017) [pdf]
Anselm Brachmann, Erhardt Barth, Christoph Redies: “Using CNN features to better understand what makes visual artworks special.” Front Psychol (2017) [pdf]
Deng Yubin, Chen Change Loy, Xiaoou Tang: “Image aesthetic assessment: An experimental survey.” IEEE Signal Processing Magazine (2017) [pdf]

2016-2006

Long Mai, Hailin Jin, Feng Liu: “Composition-preserving deep photo aesthetics assessment.” CVPR (2016) [pdf]
Yueying Kao, Ran He, Kaiqi Huang. Visual Aesthetic Quality Assessment with Multi-task Deep Learning. TIP(2016) [pdf]
Shu Kong, Xiaohui Shen, Zhe L. Lin, Radomír Mech, Charless C. Fowlkes: “Photo aesthetics ranking network with attributes and content adaptation.” ECCV (2016) [pdf] [code]
Tunc¸ Ozan Aydın, Aljoscha Smolic, and Markus Gross. Automated Aesthetic Analysis of Photographic Images. TVCG(2015) [pdf]
Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, James Z. Wang: “Deep multi-patch aggregation network for image style, aesthetics, and quality estimation.” ICCV (2015) [pdf]
Xinmei Tian, Zhe Dong, Kuiyuan Yang, Tao Mei. Query-dependent Aesthetic Model with Deep Learning for Photo Quality Assessment. TMM(2015) [pdf]
Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z. Wang: “Rapid: Rating pictorial aesthetics using deep learning.” ACM MM (2014) [pdf] [code]
Wei Luo, Xiaogang Wang, and Xiaoou Tang, Content-Based Photo Quality Assessment. TMM(2013) [pdf]
Naila Murray, Luca Marchesotti, Florent Perronnin: “AVA: A large-scale database for aesthetic visual analysis.” CVPR (2012) [pdf]
Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriela Csurka: “Assessing the aesthetic quality of photographs using generic image descriptors.” ICCV (2011) [pdf]
Masashi Nishiyama1, Takahiro Okabe1, Imari Sato. Aesthetic Quality Classification of Photographs Based on Color Harmony. CVPR(2011) [pdf]
Sagnik Dhar, Vicente Ordonez, Tamara L Berg: “High level describable attributes for predicting aesthetics and interestingness.” CVPR (2011) [pdf]
Ritendra Datta, Jia Li, and James Z. Wang: “Algorithmic inferencing of aesthetics and emotion in natural images: An exposition.” ICIP (2008) [pdf]
Ritendra Datta. Jia Li, James Z. Wang. Studying aesthetic in photographic images using a computational approach. ECCV(2006) [pdf]

数据集

images with aesthetic score/attribute

Photo.net (2006) [Dataset Description] [Annotation]
DPChallenge (2008) [Dataset Description] [Annotation]
CUHK-PQ (2011) [link]
AVA (2012) [link]
AADB (2016) [link]
FLICKER-AES and REAL-CUR (2017) [link]
PCCD (2017) [link]
AROD (2018) [link]

images with aesthetic caption

AVA-Comments (2016) [paper]
PCCD (2017) [GoogleDrive]
AVA-Reviews (2018) [paper]
DPC-Captions (2019) [link] (not available)

参考代码

titu1994/neural-image-assessment: Implementation of NIMA: Neural Image Assessment in Keras (github.com)

truskovskiyk/nima.pytorch: NIMA: Neural IMage Assessment (github.com)

参考文献

bcmi/Awesome-Aesthetic-Evaluation-and-Cropping (github.com)

图像美学质量评价技术发展趋势，科技导报2018

【完结】重磅！深度学习计算摄影的12篇干货文章

【技术综述】AVA-第一个大规模的美学质量评估数据库

【技术综述】图像美学质量评价调研报告

图像美学质量评价技术总结_God_68的博客-CSDN博客_图像美学质量评价

图像质量评价综述：美学+客观（原创）_山中有石为玉-CSDN博客

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