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AI-Generated Images (AGIs) have inherent multimodal nature.
“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.
“Making a “completely blind” image quality analyzer,”
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik, · 2012
Earlier work this paper cites.
“Generative adversarial nets,”
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, · 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.
“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.
“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.
“Improved techniques for training gans,”
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Large-scale crowdsourced study for tone-mapped hdr pictures,”
Debarati Kundu, Deepti Ghadiyaram, Alan C Bovik, and Brian L Evans, · 2017
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.
“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.
“Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network,”
Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang, · 2020
Cited alongside, same era.
“Norm-in-norm loss with faster convergence and better performance for image quality assessment,”
Dingquan Li, Tingting Jiang, and Ming Jiang, · 2020
Cited alongside, same era.
“High-resolution image synthesis with latent diffusion models,”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer, · 2022
Later among the works it cites.
“Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models,”
Zijie J Wang, Evan Montoya, David Munechika, Haoyang Yang, Benjamin Hoover, and Duen Horng Chau, · 2022
Later among the works it cites.
“A perceptual quality assessment exploration for aigc images,”
Zicheng Zhang, Chunyi Li, Wei Sun, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai, · 2023
Later among the works it cites.
“Agiqa-3k: An open database for ai-generated image quality assessment,”
Chunyi Li, Zicheng Zhang, Haoning Wu, Wei Sun, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai, and Weisi Lin, · 2023
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, · 2023
Later among the works it cites.
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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.
“Learning transferable visual models from natural language supervision,”
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., · 2021
Cited alongside, same era.
“Clipscore: A reference-free evaluation metric for image captioning,”
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi, · 2021
Cited alongside, same era.
“Blind image quality assessment via vision-language correspondence: A multitask learning perspective,”
Weixia Zhang, Guangtao Zhai, Ying Wei, Xiaokang Yang, and Kede Ma, · 2023
Later among the works it cites.
“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
Later among the works it cites.
“Better aligning text-to-image models with human preference,”
Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li, · 2023
Later among the works it cites.