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† Corresponding Authors.This work was supported by Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 62302328), Jiangsu Province Foundation for Young Scientists (Grant No. BK20230482), Suzhou Key Laboratory Open Project (Grant No. 25SZZD07) and Jiangsu Manufacturing Strong Province Construction Special Fund Project (Grant Name: Research and Development and Industrialization of Intelligent Service Robots Integrating Large Model and Multimodal Technology).Siyue Yao, and Eng Gee Lim are with the School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China (e-mail: siyue.yao2302@student.xjtlu.edu.cn; enggee.lim@xjtlu.edu.cn).Mingjie Sun, and Baojiang Zhong are with the School of Computer Science and Technology, Soochow University, Suzhou 215006, China (e-mail: mjsun@suda.edu.cn; bjzhong@suda.edu.cn).Ran Yi is with the School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: ranyi@sjtu.edu.cn).Moncef Gabbouj is with the Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33720, Finland (e-mail: moncef.gabbouj@tuni.fi)
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Spatial relationship hallucinations in image inpainting can be significantly reduced by directly optimizing for preferences on background plausibility, even when foregrounds are identical.