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Image quality assessment (IQA) is an important research topic for understanding and improving visual experience.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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Pyramid methods in image processing
Edward H Adelson, Charles H Anderson, James R Bergen, Peter J Burt, and Joan M Ogden · 1984
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Blind image quality assessment: From natural scene statistics to perceptual quality
Anush Krishna Moorthy and Alan Conrad Bovik · 2011
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Pre-trained image processing transformer
Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao · 2012
Earlier work this paper cites.
Ava: A large-scale database for aesthetic visual analysis
Naila Murray, Luca Marchesotti, and Florent Perronnin · 2012
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Unsupervised feature learning framework for no-reference image quality assessment
Peng Ye, Jayant Kumar, Le Kang, and David Doermann · 2012
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Learning without human scores for blind image quality assessment
Wufeng Xue, Lei Zhang, and Xuanqin Mou · 2013
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Convolutional neural networks for no-reference image quality assessment
Le Kang, Peng Ye, Yi Li, and David Doermann · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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A feature-enriched completely blind image quality evaluator
Lin Zhang, Lei Zhang, and Alan C Bovik · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Fully deep blind image quality predictor
Jongyoo Kim and Sanghoon Lee · 2016
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Photo aesthetics ranking network with attributes and content adaptation
Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, and Charless Fowlkes · 2016
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Composition-preserving deep photo aesthetics assessment
Long Mai, Hailin Jin, and Feng Liu · 2016
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Yfcc100m: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
Cited alongside, same era.
Blind image quality assessment based on high order statistics aggregation
Jingtao Xu, Peng Ye, Qiaohong Li, Haiqing Du, Yong Liu, and David Doermann · 2016
Cited alongside, same era.
Deep neural networks for no-reference and full-reference image quality assessment
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin · 2017
Cited alongside, same era.
Perceptual quality prediction on authentically distorted images using a bag of features approach
Deepti Ghadiyaram and Alan C Bovik · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Nima: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
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Attention augmented convolutional networks
Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Effective aesthetics prediction with multi-level spatially pooled features
Vlad Hosu, Bastian Goldlucke, and Dietmar Saupe · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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A unified probabilistic formulation of image aesthetic assessment
Hui Zeng, Zisheng Cao, Lei Zhang, and Alan C Bovik · 2019
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Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
A-lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment
Shuang Ma, Jing Liu, and Chang Wen Chen · 2017
Cited alongside, same era.
A deep architecture for unified aesthetic prediction
Naila Murray and Albert Gordo · 2017
Cited alongside, same era.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
A probabilistic quality representation approach to deep blind image quality prediction
Hui Zeng, Lei Zhang, and Alan C Bovik · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Later among the works it cites.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Perceptual quality assessment of smartphone photography
Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang · 2020
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Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment
Vlad Hosu, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe · 2020
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Blindly assess image quality in the wild guided by a self-adaptive hyper network
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang · 2020
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From patches to pictures (paq-2-piq): Mapping the perceptual space of picture quality
Zhenqiang Ying, Haoran Niu, Praful Gupta, Dhruv Mahajan, Deepti Ghadiyaram, and Alan Bovik · 2020
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Metaiqa: deep meta-learning for no-reference image quality assessment
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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