Computational Visual Media2025，Vol.11Issue(6)：P.1329-1361,33.DOI:10.26599/CVM.2025.9450408
GRIG:Data-efficient generative residual image inpainting
Wanglong Lu 1Xianta Jiang 2Xiaogang Jin 3Yong-Liang Yang 4Minglun Gong 5Kaijie Shi 1Tao Wang 6Hanli Zhao7
作者信息
- 1. Key Laboratory of Intelligent Informatics of Safety&Emergency of Zhejiang Province,Wenzhou University,Wenzhou 325035,China Department of Computer Science,Memorial University of Newfoundland,St.John''s,NL A1B 3X5,Canada
- 2. Department of Computer Science,Memorial University of Newfoundland,St.John''s,NL A1B 3X5,Canada
- 3. State Key Laboratory of CAD&CG,Zhejiang University,Hangzhou 310058,China
- 4. Department of Computer Science,University of Bath,Bath BA27AY,UK
- 5. School of Computer Science University of Guelph Guelph,ON N1G 2W1,Canada
- 6. Department of Computer Science and Technology,Nanjing University,Nanjing,China
- 7. Key Laboratory of Intelligent Informatics of Safety&Emergency of Zhejiang Province,Wenzhou University,Wenzhou 325035,China
- 折叠
摘要
关键词
image inpainting/iterative reasoning/residual learning/generative adversarial networks分类
信息技术与安全科学引用本文复制引用
Wanglong Lu,Xianta Jiang,Xiaogang Jin,Yong-Liang Yang,Minglun Gong,Kaijie Shi,Tao Wang,Hanli Zhao..GRIG:Data-efficient generative residual image inpainting[J].Computational Visual Media,2025,11(6):P.1329-1361,33.基金项目
supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ21F020001) （Grant No.LZ21F020001）
the Basic Scientific Research Program of Wenzhou(Grant No.S20220018). （Grant No.S20220018）
