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论文
2021
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Hao Lou, Heng Huang，Chaoen Xiao and Xin Jin：Aesthetic Evaluation and Guidance for Mobile Photography. MM(2021) [pdf]
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Pei Lv, Jianqi Fan, Xixi Nie, Weiming Dong. User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning. TMM(2021) [pdf]
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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]
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Dongyu She, Yu-Kun Lai, Gaoxiong Yi, Kun Xu: “Hierarchical layout-Aware graph convolutional network for unified aesthetics assessment.” CVPR (2021) [pdf]
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Jingwen Hou, Sheng Yang, Weisi Lin, Baoquan Zhao, and Yuming Fang. Learning Image Aesthetic Assessment from Object-level Visual Components. TIP(2021) [pdf]
2020
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Jingwen Hou, Sheng Yang, Weisi Lin:“Object-level attention for aesthetic rating distribution prediction.” ACM MM (2020) [pdf]
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Munan Xu, Jia-Xing Zhong, Yurui Ren. Context-aware Attention Network for Predicting Image Aesthetic Subjectivity. MM(2020) [pdf]
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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]
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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]
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Hui Zeng, Zisheng Cao, Lei Zhang, Alan C. Bovik: “A unified probabilistic formulation of image aesthetic assessment.” TIP (2020) [pdf] [code]
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Dong Liu, Rohit Puri, Nagendra Kamath, Subhabrata Bhattacharya: “Composition-aware image aesthetics assessment.” WACV(2020) [pdf]
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Qi Kuang , Xin Jin , Qinping Zhao, and Bin Zhou. Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment. TMM(2020) [pdf]
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Lijie Wang, Xueting Wang, Toshihiko Yamasaki. Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents. arXiv(2020) [pdf]
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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
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Weining Wang, Rui Deng: “Modeling human perception for image aesthetic assessme.” ICIP (2019) [pdf]
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JUN-TAE LEE1, CHUL LEE, AND CHANG-SU KIM. Property-Specific Aesthetic Assessment With Unsupervised Aesthetic Property Discovery. Acess(2019) [pdf]
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Vlad Hosu, Bastian GoldluModeling human perception for image aesthetic assessmen. multi-level spatially pooled features." CVPR (2019) [pdf] [code]
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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]
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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]
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Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, Hongyan Jiang, Allen Tan: “Personality driven multi-task learning for image aesthetic assessment.” ICME (2019) [pdf]
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Ning Ma, Alexey Volkov, Aleksandr Livshits, Pawel Pietrusinski, Houdong Hu, Mark Bolin: “An universal image attractiveness ranking framework.” WACV (2019) [pdf]
2018
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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]
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Andrew Gilbert, John Collomosse, Hailin Jin, and Brian Price. Disentangling Structure and Aesthetics for Style-aware Image Completion. CVPR(2018) [pdf]
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Xiaodan Zhang, Xinbo Gao, Wen Lu, and Lihuo He. A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction. TMM(2018) [pdf]
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Katja Thömmes* and Ronald Hübner: “Instagram likes for architectural photos can be predicted by quantitative balance measures and curvature.” Front Psychol (2018) [pdf]
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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]
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Ning Yu, Xiaohui Shen, Zhe Lin, Radomir Mech, Connelly Barnes: “Learning to detect multiple photographic defects.” WACV (2018) [pdf]
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Keunsoo Ko, Jun Tae Lee, Chang-Su Kim: “PAC-Net: Pairwise aesthetic comparison network for image aesthetic assessment.” ICIP (2018) [pdf]
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Hossein Talebi and Peyman Milanfar: “NIMA: Neural image assessment.” TIP (2018) [pdf] [code]
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Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch: “Will people like your image? Learning the aesthetic space.” WACV (2018) [pdf] [code]
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Guolong Wang, Junchi Yan, Zheng Qin: “Collaborative and attentive learning for personalized image aesthetic assessment.” IJCAI (2018) [pdf]
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Xin Jin, Le Wu, Xiaodong Li, Siyu Chen. Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence. AAAI(2018) [pdf]
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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]
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Wenguan Wang, Jianbing Shen, and Haibin Ling. A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping. TPAMI(2018) [pdf]
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Michal Kucer, David W. Messinger. Aesthetic Inference for Smart Mobile Devices. WACV(2018) [pdf]
2017
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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]
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Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, David J. Foran: “Personalized image aesthetics.” ICCV (2017) [pdf] [code]
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Anselm Brachmann and Christoph Redies: “Computational and experimental approaches to visual aesthetics.” Front Hum Neurosci (2017) [pdf]
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Naila Murray and Albert Gordo. A deep architecture for unified aesthetic prediction. arXiv(2017) [pdf]
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Anselm Brachmann, Erhardt Barth, Christoph Redies: “Using CNN features to better understand what makes visual artworks special.” Front Psychol (2017) [pdf]
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Deng Yubin, Chen Change Loy, Xiaoou Tang: “Image aesthetic assessment: An experimental survey.” IEEE Signal Processing Magazine (2017) [pdf]
2016-2006
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Long Mai, Hailin Jin, Feng Liu: “Composition-preserving deep photo aesthetics assessment.” CVPR (2016) [pdf]
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Yueying Kao, Ran He, Kaiqi Huang. Visual Aesthetic Quality Assessment with Multi-task Deep Learning. TIP(2016) [pdf]
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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]
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Tunc¸ Ozan Aydın, Aljoscha Smolic, and Markus Gross. Automated Aesthetic Analysis of Photographic Images. TVCG(2015) [pdf]
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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]
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Xinmei Tian, Zhe Dong, Kuiyuan Yang, Tao Mei. Query-dependent Aesthetic Model with Deep Learning for Photo Quality Assessment. TMM(2015) [pdf]
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Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z. Wang: “Rapid: Rating pictorial aesthetics using deep learning.” ACM MM (2014) [pdf] [code]
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Wei Luo, Xiaogang Wang, and Xiaoou Tang, Content-Based Photo Quality Assessment. TMM(2013) [pdf]
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Naila Murray, Luca Marchesotti, Florent Perronnin: “AVA: A large-scale database for aesthetic visual analysis.” CVPR (2012) [pdf]
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Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriela Csurka: “Assessing the aesthetic quality of photographs using generic image descriptors.” ICCV (2011) [pdf]
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Masashi Nishiyama1, Takahiro Okabe1, Imari Sato. Aesthetic Quality Classification of Photographs Based on Color Harmony. CVPR(2011) [pdf]
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Sagnik Dhar, Vicente Ordonez, Tamara L Berg: “High level describable attributes for predicting aesthetics and interestingness.” CVPR (2011) [pdf]
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Ritendra Datta, Jia Li, and James Z. Wang: “Algorithmic inferencing of aesthetics and emotion in natural images: An exposition.” ICIP (2008) [pdf]
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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
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Photo.net (2006) [Dataset Description] [Annotation]
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DPChallenge (2008) [Dataset Description] [Annotation]
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CUHK-PQ (2011) [link]
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AVA (2012) [link]
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AADB (2016) [link]
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FLICKER-AES and REAL-CUR (2017) [link]
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PCCD (2017) [link]
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AROD (2018) [link]
images with aesthetic caption
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AVA-Comments (2016) [paper]
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PCCD (2017) [GoogleDrive]
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AVA-Reviews (2018) [paper]
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DPC-Captions (2019) [link] (not available)
参考代码
truskovskiyk/nima.pytorch: NIMA: Neural IMage Assessment (github.com)
相关研究人员主页
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
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