Awesome

Awesome Image Aesthetic Assessment and Cropping

A curated list of resources including papers, datasets, and relevant links pertaining to aesthetic evaluation and cropping.

Contributing

Contributions are welcome. If you wish to contribute, feel free to send a pull request. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a pull request.

Table of Contents

• Papers
• Datasets
• Other Resources

Papers

Image Aesthetic Assessment

• Shuai He, Anlong Ming, Yaqi Li, Jinyuan Sun, ShunTian Zheng, Huadong Ma: "Thinking Image Color Aesthetics Assessment: Models, Datasets and Benchmarks" ICCV (2023) [paper] [homepage]
• Shuai He, Anlong Ming, Shuntian Zheng, Haobin Zhong, Huadong Ma: "EAT: An Enhancer for Aesthetics-Oriented Transformers." ACM MM (2023) [pdf] [homepage]
• Yaohui Li, Yuzhe Yang, Huaxiong Li,Haoxing Chen, Liwu Xu, Leida Li, Yaqian Li, Yandong Guo: "Transductive Aesthetic Preference Propagation for Personalized Image Aesthetics Assessment" ACM MM (2023) [pdf]
• Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai, Paul L. Rosin: "Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method" CVPR (2023) [pdf] [dataset]
• Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang: "VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining" CVPR (2023) [pdf]
• Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming: "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks" IJCAI (2022) [pdf] [code]
• Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, Yandong Guo: "Personalized Image Aesthetics Assessment with Rich Attributes" CVPR (2022) [pdf] [homepage]
• Dongyu She, Yu-Kun Lai, Gaoxiong Yi, Kun Xu: "Hierarchical layout-aware graph convolutional network for unified aesthetics assessment." CVPR (2021) [pdf]
• Hao Lou, Heng Huang, Chaoen Xiao, Xin Jin: "Aesthetic Evaluation and Guidance for Mobile Photography." ACM MM(2021) [pdf]
• Pei Lv, Jianqi Fan, Xixi Nie, Weiming Dong, Xiaoheng Jiang, Bing Zhou, Mingliang Xu, Changsheng Xu: "User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning." TMM (2021) [pdf]
• Jingwen Hou, Sheng Yang, Weisi Lin, Baoquan Zhao, Yuming Fang: "Learning Image Aesthetic Assessment from Object-level Visual Components." TIP (2021) [pdf]
• Lin Zhao, Meimei Shang, Fei Gao, Rongsheng Li, Fei Huang, Jun Yu: "Representation learning of image composition for aesthetic prediction." CVIU (2020) [pdf] [code]
• Jingwen Hou, Sheng Yang, Weisi Lin: "Object-level attention for aesthetic rating distribution prediction." ACM 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]
• Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, Guangming Shi: "Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization." IEEE Trans. Cybern. (2020) [pdf] [code]
• Weining Wang, Rui Deng: "Modeling human perception for image aesthetic assessme." ICIP (2019) [pdf]
• Vlad Hosu, Bastian Goldlucke, Dietmar Saupe: "Effective aesthetics prediction with multi-level spatially pooled features." CVPR (2019) [pdf] [code]
• 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]
• 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]
• 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]
• 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]
• 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]
• Long Mai, Hailin Jin, Feng Liu: "Composition-preserving deep photo aesthetics assessment." CVPR (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]
• 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]
• Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z. Wang: "Rapid: Rating pictorial aesthetics using deep learning." ACM MM (2014) [pdf] [code]
• 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]
• 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]

Image Cropping

• Zhiyu Pan, Jiahao Cui, Kewei Wang, Yizheng Wu, and Zhiguo Cao. “Pseudo Label Fusion with Uncertainty Estimation for Semi-Supervised Cropping Box Regression.” TMM (2024) [pdf]
• Yukun Su, Yiwen Cao, Jingliang Deng, Fengyun Rao, and Qingyao Wu. “Spatial-Semantic Collaborative Cropping for User Generated Content.” AAAI (2024) [pdf] [code]
• Quan Yuan, Leida Li, and Pengfei Chen. “Aesthetic Image Cropping Meets Vlp: Enhancing Good While Reducing Bad.” SSRN (2024) [pdf]
• James Hong, Lu Yuan, Michaël Gharbi, Matthew Fisher, and Kayvon Fatahalian. “Learning Subject-Aware Cropping by Outpainting Professional Photos.” AAAI (2024) [pdf] [code]
• Zhiyu Pan, Yinpeng Chen, Jiale Zhang, Hao Lu, Zhiguo Cao, and Weicai Zhong. “Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression.” AAAI (2023) [pdf]
• GuoYe Yang, WenYang Zhou, Yun Cai, SongHai Zhang, and FangLue Zhang. “Focusing on Your Subject: Deep Subject-Aware Image Composition Recommendation Networks.” Computational Visual Media (2023) [pdf] [dataset]
• Takumi Nishiyasu, Wataru Shimoda, and Yoichi Sato. “Image Cropping under Design Constraints.” ACMMM Asia (2023) [pdf] [code]
• Tengfei Shi, Chenglizhao Chen, Yuanbo He, Wenfeng Song, and Aimin Hao. “Joint Probability Distribution Regression for Image Cropping.” ICIP (2023) [pdf]
• Xiaoyu Liu, Ming Liu, Junyi Li, Shuai Liu, Xiaotao Wang, Lei Lei, Wangmeng Zuo: "Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition." ICCV (2023) [pdf] [code]
• Zhihang Zhong, Mingxi Cheng, Zhirong Wu, Yuhui Yuan, Yinqiang Zheng, Ji Li, Han Hu, Stephen Lin, Yoichi Sato, Imari Sato: "ClipCrop: Conditioned Cropping Driven by Vision-Language Model." ICCV Workshops (2023) [pdf]
• Wang Chao, Li Niu, Bo Zhang, Liqing Zhang: "Image Cropping with Spatial-aware Feature and Rank Consistency." CVPR (2023) [pdf]
• Gengyun Jia, Huaibo Huang, Chaoyou Fu, Ran He: "Rethinking Image Cropping: Exploring Diverse Compositions From Global Views." CVPR (2022) [pdf]
• Yang Cheng, Qian Lin, Jan P. Allebach: "Re-Compose the Image by Evaluating the Crop on More Than Just a Score." WACV (2022) [pdf]
• Zhiyu Pan, Zhiguo Cao, Kewei Wang, Hao Lu, Weicai Zhong: "TransView: Inside, Outside, and Across the Cropping View Boundaries." ICCV (2021) [pdf]
• Lei Zhong, Feng-Heng Li, Hao-Zhi Huang, Yong Zhang, Shao-Ping Lu, Jue Wang: "Aesthetic-guided outward image cropping." TOG (2021) [pdf]
• Chaoyi Hong, Shuaiyuan Du, Ke Xian, Hao Lu, Zhiguo Cao, Weicai Zhong: "Composing photos like a photographer." CVPR (2021) [pdf] [code]
• Debang Li, Junge Zhang, Kaiqi Huang: "Learning to learn cropping models for different aspect ratio requirements." CVPR (2020) [pdf]
• Debang Li, Junge Zhang, Kaiqi Huang, Ming-Hsuan Yang: "Composing good shots by exploiting mutual relations." CVPR (2020) [pdf] [code]
• Yi Tu, Li Niu, Weijie Zhao, Dawei Cheng, Liqing Zhang: "Image cropping with composition and saliency aware aesthetic score map." AAAI (2020) [pdf]
• Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang: "Grid anchor based image cropping: a new benchmark and an efficient model." TPAMI (2020) [pdf] [code]
• Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang: "Reliable and efficient image cropping: a grid anchor based approach." CVPR (2019) [pdf] [code]
• Weirui Lu, Xiaofen Xing, Bolun Cai, Xiangmin Xu: "Listwise view ranking for image cropping." IEEE Access (2019) [pdf] [code]
• Zijun Wei, Jianming Zhang, Xiaohui Shen, Zhe Lin, Radomír Mech, Minh Hoai, Dimitris Samaras: "Good view hunting: learning photo composition from dense view pairs." CVPR (2018) [pdf] [VEN code] [VPN code]
• Debang Li, Huikai Wu, Junge Zhang, Kaiqi Huang: "A2-RL: aesthetics aware reinforcement learning for image cropping." [pdf] [code]
• Seyed A. Esmaeili, Bharat Singh, Larry S. Davis: "Fast-At: Fast automatic thumbnail generation using deep neural networks." CVPR (2017) [pdf]
• Wenguan Wang, Jianbing Shen: "Deep cropping via attention box prediction and aesthetics assessment." ICCV (2017) [pdf]
• Yi-Ling Chen, Jan Klopp, Min Sun, Shao-Yi Chien, Kwan-Liu Ma: "Learning to compose with professional photographs on the web." ACM MM (2017) [pdf] [code]
• Yi-Ling Chen, Tzu-Wei Huang, Kai-Han Chang, Yu-Chen Tsai, Hwann-Tzong Chen, Bing-Yu Chen: "Quantitative analysis of automatic image cropping algorithms: a dataset and comparative study." WACV (2017) [pdf]
• Jiansheng Chen, Gaocheng Bai, Shaoheng Liang, Zhengqin Li: "Automatic image cropping: a computational complexity study." CVPR (2016) [pdf]
• Jonas Abeln, Leonie Fresz, Seyed Ali Amirshahi, Chris McManus, Michael Koch, Helene Kreysa, Christoph Redies: "Preference for well-balanced saliency in details cropped from photographs." Front Hum Neurosci (2016) [pdf]
• Chen Fang, Zhe Lin, Radomír Mech, Xiaohui Shen: "Automatic image Cropping using visual composition, boundary simplicity and content preservation models." ACM MM (2014) [pdf]
• Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang: "Learning the change for automatic image cropping." CVPR (2013) [pdf]
• Bongwon Suh, Haibin Ling, Benjamin B. Bederson, David W. Jacobs: "Automatic thumbnail cropping and its effectiveness." UIST (2003) [pdf]

Aesthetic Captioning

• Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang: "VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining." CVPR (2023) [pdf]
• Koustav Ghosal, Aakanksha Rana, Aljosa Smolic: "Aesthetic Image Captioning From Weakly-Labelled Photographs." ICCVW (2019) [pdf] [homepage]
• 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] [code]
• Wenshan Wang, Su Yang, Weishan Zhang, Jiulong Zhang: "Neural aesthetic image reviewer." IET Computer Vision (2019) [pdf]
• Kuang-Yu Chang, Kung-Hung Lu, Chu-Song Chen: "Aesthetic Critiques Generation for Photos." ICCV (2017) [pdf] [code]
• Ye Zhou, Xin Lu, Junping Zhang, James Z. Wang: "Joint image and text representation for aesthetics analysis." ACM MM (2016) [pdf]

Datasets

Aesthetic Assessment Datasets

images with aesthetic score/attribute

• Photo.net (2006) [homepage]
• DPChallenge (2008) [homepage]
• CUHK-PQ (2011) [homepage]
• AVA (2012) [download]
• AADB (2016) [homepage]
• FLICKER-AES and REAL-CUR (2017) [homepage]
• PCCD (2017) [homepage]
• AROD (2018) [homepage]
• EVA (2020) [homepage]
• TAD66K (2022) [homepage]
• PARA (2022) [homepage]
• Boldbrush Artistic Image Dataset (BAID) (2023) [homepage]
• Largest Color-oriented Dataset: ICAA17K (2023) [homepage]

images with aesthetic caption

• PCCD (2017) [download]
• DPC-Captions (2019) [homepage]
• AVA-Captions (2019) [homepage]

image with composition score/label

• KU-PCP (2018) [homepage]
• CADB (2021) [homepage]

Image Cropping Datasets

densely annotated (multiple crops in each image are annotated)

• CPC (2018) [homepage]
• GAICD (2019) [homepage]
• SACD (2023) [homepage]
• SID (2024) [homepage]
• UGCrop5K (2024) [homepage]

sparsely annotated (only the best crop in each image is annotated)

• ICDB/MSR-ICD (2013) [homepage]
• FLMS/HCDB (2014) [download images] [download crops]
• FCDB (2017) [homepage]
• GNMC (2022) [homepage]