Fetching the paper…
Reading the bibliography…
DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning.
A law of comparative judgment
Louis L Thurstone · 1927
Earlier work this paper cites.
Models for distributions on permutations
Hal Stern · 1990
Earlier work this paper cites.
Blind measurement of blocking artifacts in images
Zhou Wang, Alan C Bovik, and Brian L Evan · 2000
Earlier work this paper cites.
Local phase coherence and the perception of blur
Zhou Wang and Eero P Simoncelli · 2003
Earlier work this paper cites.
Image quality assessment: From error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
Earlier work this paper cites.
Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
Earlier work this paper cites.
Learning to rank: From pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li · 2007
Earlier work this paper cites.
FRank: A ranking method with fidelity loss
Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma · 2007
Earlier work this paper cites.
Human age estimation using bio-inspired features
Guodong Guo, Guowang Mu, Yun Fu, and Thomas S Huang · 2009
Earlier work this paper cites.
A no-reference metric for perceived ringing artifacts in images
Hantao Liu, Nick Klomp, and Ingrid Heynderickx · 2009
Earlier work this paper cites.
No-reference blur assessment of digital pictures based on multifeature classifiers
Alexandre Ciancio, André Luiz N Targino Targino da Costa, Eduardo A. B. da Silva, Amir Said, Ramin Samadani, and Pere Obrador · 2010
Earlier work this paper cites.
Reduced-and no-reference image quality assessment
Zhou Wang and Alan C Bovik · 2011
Earlier work this paper cites.
No-reference image quality assessment in the spatial domain
Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik · 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.
A no-reference metric for evaluating the quality of motion deblurring
Yiming Liu, Jue Wang, Sunghyun Cho, Adam Finkelstein, and Szymon Rusinkiewicz · 2013
Earlier work this paper cites.
Content-based photo quality assessment
Xiaoou Tang, Wei Luo, and Xiaogang Wang · 2013
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.
Learning to rank for blind image quality assessment
Fei Gao, Dacheng Tao, Xinbo Gao, and Xuelong Li · 2015
Earlier work this paper cites.
Massive online crowdsourced study of subjective and objective picture quality
Deepti Ghadiyaram and Alan C Bovik · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Perceptual quality prediction on authentically distorted images using a bag of features approach
Deepti Ghadiyaram and Alan C Bovik · 2017
Cited alongside, same era.
RankIQA: Learning from rankings for no-reference image quality assessment
Xialei Liu, Joost Van De Weijer, and Andrew D Bagdanov · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs
Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, and Dacheng Tao · 2017
Cited alongside, same era.
End-to-end blind image quality assessment using deep neural networks
Kede Ma, Wentao Liu, Kai Zhang, Zhengfang Duanmu, Zhou Wang, and Wangmeng Zuo · 2017
Cited alongside, same era.
NIMA: Neural image assessment
Hossein Talebi and Peyman Milanfar · 2018
MANIQA: Multi-dimension attention network for no-reference image quality assessment
Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang · 2022
Later among the works it cites.
Continual learning for blind image quality assessment
Weixia Zhang, Dingquan Li, Chao Ma, Guangtao Zhai, Xiaokang Yang, and Kede Ma · 2022
Later among the works it cites.
DreamSim: Learning new dimensions of human visual similarity using synthetic data
Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola · 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.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Mallows and generalized mallows model for matchings
Ekhine Irurozki, Borja Calvo, and Jose A. Lozano · 2019
Cited alongside, same era.
KADID-10K: A large-scale artificially distorted IQA database
Hanhe Lin, Vlad Hosu, and Dietmar Saupe · 2019
Cited alongside, same era.
Quality evaluation of image dehazing methods using synthetic hazy images
Xiongkuo Min, Guangtao Zhai, Ke Gu, Yucheng Zhu, Jiantao Zhou, Guodong Guo, Xiaokang Yang, Xinping Guan, and Wenjun Zhang · 2019
Cited alongside, same era.
Fast differentiable sorting and ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet, and Josip Djolonga · 2020
Cited alongside, same era.
Perceptual quality assessment of smartphone photography
Yuming Fang, Hanwei Zhu, Yan Zeng, Kede Ma, and Zhou Wang · 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.
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.
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
Later among the works it cites.
DeepSeekMath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and Daya Guo · 2024
Later among the works it cites.
Q-Instruct: Improving low-level visual abilities for multi-modality foundation models
Haoning Wu, Zicheng Zhang, Erli Zhang, Chaofeng Chen, Liang Liao, Annan Wang, Kaixin Xu, Chunyi Li, Jingwen Hou, Guangtao Zhai, Geng Xue, Wenxiu Sun, Qiong Yan, and Weisi Lin · 2024
Later among the works it cites.
Q-ALIGN: Teaching LMMs for visual scoring via discrete text-defined levels
Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, Qiong Yan, Xiongkuo Min, Guangtao Zhai, and Weisi Lin · 2024
Later among the works it cites.
A comprehensive study of multimodal large language models for image quality assessment
Tianhe Wu, Kede Ma, Jie Liang, Yujiu Yang, and Lei Zhang · 2024
Later among the works it cites.
Boosting image quality assessment through efficient Transformer adaptation with local feature enhancement
Kangmin Xu, Liang Liao, Jing Xiao, Chaofeng Chen, Haoning Wu, Qiong Yan, and Weisi Lin · 2024
Later among the works it cites.
Depicting beyond scores: Advancing image quality assessment through multi-modal language models
Zhiyuan You, Zheyuan Li, Jinjin Gu, Zhenfei Yin, Tianfan Xue, and Chao Dong · 2024
Later among the works it cites.
Adaptive image quality assessment via teaching large multimodal model to compare
Hanwei Zhu, Haoning Wu, Yixuan Li, Zicheng Zhang, Baoliang Chen, Lingyu Zhu, Yuming Fang, Guangtao Zhai, Weisi Lin, and Shiqi Wang · 2024
Later among the works it cites.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al · 2025
Closest in time.
Toward generalized image quality assessment: Relaxing the perfect reference quality assumption
Du Chen, Tianhe Wu, Kede Ma, and Lei Zhang · 2025
Closest in time.
DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
Closest in time.
Q-Insight: Understanding image quality via visual reinforcement learning
Weiqi Li, Xuanyu Zhang, Shijie Zhao, Yabin Zhang, Junlin Li, Li Zhang, and Jian Zhang · 2025
Closest in time.
Teaching large language models to regress accurate image quality scores using score distribution
Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong · 2025
Closest in time.