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With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc.
“Methodology for the subjective assessment of the quality of television pictures,”
I. T. Union, · 2002
Earlier work this paper cites.
“A statistical evaluation of recent full reference image quality assessment algorithms,”
Hamid R Sheikh, Muhammad F Sabir, and Alan C Bovik, · 2006
Earlier work this paper cites.
“A no-reference perceptual image sharpness metric based on a cumulative probability of blur detection,”
Niranjan D Narvekar and Lina J Karam, · 2009
Earlier work this paper cites.
“Libsvm: a library for support vector machines,”
Chih-Chung Chang and Chih-Jen Lin, · 2011
Earlier work this paper cites.
“Visual-textual joint relevance learning for tag-based social image search,”
Yue Gao, Meng Wang, Zheng-Jun Zha, Jialie Shen, Xuelong Li, and Xindong Wu, · 2012
Earlier work this paper cites.
“Making a “completely blind” image quality analyzer,”
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik, · 2012
Earlier work this paper cites.
“Hybrid no-reference quality metric for singly and multiply distorted images,”
Ke Gu, Guangtao Zhai, Xiaokang Yang, and Wenjun Zhang, · 2014
Earlier work this paper cites.
“Models of word segmentation in fluent maternal speech to infants,”
Richard N Aslin, Julide Z Woodward, and Nicholas P LaMendola, · 2014
Earlier work this paper cites.
“Microsoft coco: Common objects in context,”
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick, · 2014
Earlier work this paper cites.
“Blind image quality assessment using joint statistics of gradient magnitude and laplacian features,”
Wufeng Xue, Xuanqin Mou, Lei Zhang, Alan C Bovik, and Xiangchu Feng, · 2014
Earlier work this paper cites.
“Convolutional neural networks for no-reference image quality assessment,”
Le Kang, Peng Ye, Yi Li, and David Doermann, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
“Generative adversarial text to image synthesis,”
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee, · 2016
Earlier work this paper cites.
“Improved techniques for training gans,”
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Earlier work this paper cites.
“Joint chroma downsampling and upsampling for screen content image,”
Shiqi Wang, Ke Gu, Siwei Ma, and Wen Gao, · 2016
Earlier work this paper cites.
“Improved techniques for training gans,”
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Earlier work this paper cites.
“Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,”
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas, · 2017
Earlier work this paper cites.
“Gans trained by a two time-scale update rule converge to a local nash equilibrium,”
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter, · 2017
Earlier work this paper cites.
“The konstanz natural video database (konvid-1k),”
Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, Tamás Szirányi, Shujun Li, and Dietmar Saupe, · 2017
Earlier work this paper cites.
“Large-scale crowdsourced study for high dynamic range images,”
D Kundu, D Ghadiyaram, AC Bovik, and BL Evans, · 2017
Earlier work this paper cites.
“Attngan: Fine-grained text to image generation with attentional generative adversarial networks,”
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He, · 2018
Earlier work this paper cites.
Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton, · 2018
Earlier work this paper cites.
“Assessing visual quality of omnidirectional videos,”
Mai Xu, Chen Li, Zhenzhong Chen, Zulin Wang, and Zhenyu Guan, · 2018
Cited alongside, same era.
“Nima: Neural image assessment,”
Hossein Talebi and Peyman Milanfar, · 2018
Cited alongside, same era.
“Blind image quality estimation via distortion aggravation,”
Xiongkuo Min, Guangtao Zhai, Ke Gu, Yutao Liu, and Xiaokang Yang, · 2018
Cited alongside, same era.
“Blind image quality assessment using a deep bilinear convolutional neural network,”
Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang, · 2018
Cited alongside, same era.
“No-reference quality assessment of contrast-distorted images using contrast enhancement,”
Jia Yan, Jie Li, and Xin Fu, · 2019
Cited alongside, same era.
“Perceptual image quality assessment: a survey,”
“Hierarchical feature aggregation based on transformer for image-text matching,”
Xinfeng Dong, Huaxiang Zhang, Lei Zhu, Liqiang Nie, and Li Liu, · 2022
Later among the works it cites.
“Discrete joint semantic alignment hashing for cross-modal image-text search,”
Song Wang, Huan Zhao, and Keqin Li, · 2022
Later among the works it cites.
“Image quality score distribution prediction via alpha stable model,”
Yixuan Gao, Xiongkuo Min, Wenhan Zhu, Xiao-Ping Zhang, and Guangtao Zhai, · 2022
Later among the works it cites.
“Exploring clip for assessing the look and feel of images,”
Jianyi Wang, Kelvin CK Chan, and Chen Change Loy, · 2022
Later among the works it cites.
“IQA-PyTorch: Pytorch toolbox for image quality assessment,” [Online]. Available: https://github.com/chaofengc/IQA-PyTorch , 2022
Chaofeng Chen and Jiadi Mo, · 2022
Later among the works it cites.
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Guangtao Zhai and Xiongkuo Min, · 2020
Cited alongside, same era.
“Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment,”
Vlad Hosu, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe, · 2020
Cited alongside, same era.
“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
Cited alongside, same era.
“Adversarial text-to-image synthesis: A review,”
Stanislav Frolov, Tobias Hinz, Federico Raue, Jörn Hees, and Andreas Dengel, · 2021
Cited alongside, same era.
“Cogview: Mastering text-to-image generation via transformers,”
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al., · 2021
Cited alongside, same era.
“Zero-shot text-to-image generation,”
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever, · 2021
Cited alongside, same era.
“Screen content quality assessment: overview, benchmark, and beyond,”
Xiongkuo Min, Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang, Patrick Le Callet, and Chang Wen Chen, · 2021
Cited alongside, same era.
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon, · 2023
Closest in time.
“Rectified wasserstein generative adversarial networks for perceptual image restoration,”
Haichuan Ma, Dong Liu, and Feng Wu, · 2023
Closest in time.
“A perceptual quality assessment exploration for aigc images,”
Zicheng Zhang, Chunyi Li, Wei Sun, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai, · 2023
Closest in time.
“Imagereward: Learning and evaluating human preferences for text-to-image generation,”
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong, · 2023
Closest in time.
“Pick-a-pic: An open dataset of user preferences for text-to-image generation,”
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy, · 2023
Closest in time.
“Better aligning text-to-image models with human preference,”
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li, · 2023
Closest in time.
“Learning to evaluate the artness of ai-generated images,”
Junyu Chen, Jie An, Hanjia Lyu, and Jiebo Luo, · 2023
Closest in time.
“A real-time blind quality-of-experience assessment metric for http adaptive streaming,”
Chunyi Li, May Lim, Abdelhak Bentaleb, and Roger Zimmermann, · 2023
Closest in time.
“Light-vqa: A multi-dimensional quality assessment model for low-light video enhancement,”
Yunlong Dong, Xiaohong Liu, Yixuan Gao, Xunchu Zhou, Tao Tan, and Guangtao Zhai, · 2023
Closest in time.
“Vdpve: Vqa dataset for perceptual video enhancement,”
Yixuan Gao, Yuqin Cao, Tengchuan Kou, Wei Sun, Yunlong Dong, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai, · 2023
Closest in time.
“Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training,”
Wei Sun, Xiongkuo Min, Danyang Tu, Siwei Ma, and Guangtao Zhai, · 2023
Closest in time.
Yixiong Chen, · 2023
Closest in time.
“Clip-vip: Adapting pre-trained image-text model to video-language alignment,”
Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo, · 2023
Closest in time.
“Midjourney,” https://www.midjourney.com/ , 2023
David Holz, · 2023
Closest in time.
“D2former: Jointly learning hierarchical detectors and contextual descriptors via agent-based transformers,”
Jianfeng He, Yuan Gao, Tianzhu Zhang, Zhe Zhang, and Feng Wu, · 2023
Closest in time.
“Prompt-based learning for unpaired image captioning,”
Peipei Zhu, Xiao Wang, Lin Zhu, Zhenglong Sun, Wei-Shi Zheng, Yaowei Wang, and Changwen Chen, · 2023
Closest in time.