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Hierarchical Curriculum Learning for No-Reference Image Quality Assessment

文献类型：期刊论文

作者Wang, Juan1; Chen, Zewen1,2; Yuan, Chunfeng1; Li, Bing1,3; Ma, Wentao4; Hu, Weiming1,2,5
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2023-07-25
页码20
ISSN号0920-5691
关键词No-reference image quality assessment Hierarchical curriculum learning Prior knowledge Cross-dataset quality assessment correlation
DOI10.1007/s11263-023-01851-5
通讯作者Yuan, Chunfeng(cfyuan@nlpr.ia.ac.cn)
英文摘要Despite remarkable success has been achieved by convolutional neural networks (CNNs) in no-reference image quality assessment (NR-IQA), there still exist many challenges in improving the performance of IQA for authentically distorted images. An important factor is that the insufficient annotated data limits the training of high-capacity CNNs to accommodate diverse distortions, complicated semantic structures and high-variance quality scores of these images. To address this problem, this paper proposes a hierarchical curriculum learning (HCL) framework for NR-IQA. The main idea of the proposed framework is to leverage the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. Experimental results show that our model achieves state-of-the-art performance on multiple standard authentic IQA datasets. Moreover, the generalization of our model is fully validated by the cross-dataset evaluation and the gMAD competition. In addition, extensive analyses prove that the proposed HCL framework is effective in improving the performance of our model.
WOS关键词STATISTICS
资助项目National Key Research and Development Program of China[2020AAA0106800] ; Natural Science Foundation of China[62202470] ; Natural Science Foundation of China[61972397] ; Natural Science Foundation of China[62122086] ; Natural Science Foundation of China[U1936204] ; Natural Science Foundation of China[62036011] ; Natural Science Foundation of China[62192782] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[U2033210] ; Beijing Natural Science Foundation[4224093] ; Beijing Natural Science Foundation[JQ21017] ; Beijing Natural Science Foundation[L223003] ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research[2017KZDXM081] ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research[2018KZDXM066] ; Guangdong Provincial University Innovation Team Project[2020KCXTD045] ; Youth Innovation Promotion Association, CAS
WOS研究方向Computer Science
语种英语
出版者SPRINGER
WOS记录号WOS:001035491100001
资助机构National Key Research and Development Program of China ; Natural Science Foundation of China ; Beijing Natural Science Foundation ; Major Projects of Guangdong Education Department for Foundation Research and Applied Research ; Guangdong Provincial University Innovation Team Project ; Youth Innovation Promotion Association, CAS
源URL[http://ir.ia.ac.cn/handle/173211/53855]  
专题多模态人工智能系统全国重点实验室
通讯作者Yuan, Chunfeng
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.People AI Inc, Beijing 100080, Peoples R China
4.OPPO Corp LTD, Shanghai 201615, Peoples R China
5.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Juan,Chen, Zewen,Yuan, Chunfeng,et al. Hierarchical Curriculum Learning for No-Reference Image Quality Assessment[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:20.
APA Wang, Juan,Chen, Zewen,Yuan, Chunfeng,Li, Bing,Ma, Wentao,&Hu, Weiming.(2023).Hierarchical Curriculum Learning for No-Reference Image Quality Assessment.INTERNATIONAL JOURNAL OF COMPUTER VISION,20.
MLA Wang, Juan,et al."Hierarchical Curriculum Learning for No-Reference Image Quality Assessment".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):20.

入库方式： OAI收割

来源：自动化研究所

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