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Hierarchical Curriculum Learning for No-Reference Image Quality Assessment | |
Wang, Juan1; Chen, Zewen1,2; Yuan, Chunfeng1![]() ![]() ![]() | |
发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER VISION
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ISSN | 0920-5691 |
2023-07-25 | |
页码 | 20 |
通讯作者 | Yuan, Chunfeng(cfyuan@nlpr.ia.ac.cn) |
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. |
关键词 | No-reference image quality assessment Hierarchical curriculum learning Prior knowledge Cross-dataset quality assessment correlation |
DOI | 10.1007/s11263-023-01851-5 |
关键词[WOS] | STATISTICS |
收录类别 | SCI |
语种 | 英语 |
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 |
项目资助者 | 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 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:001035491100001 |
出版者 | SPRINGER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53856 |
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.OPPO Corp LTD, Shanghai 201615, Peoples R China 4.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 5.People AI Inc, Beijing 100080, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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