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White Box AI seminar
A New Semantic-Guided Coarse-to-Fine Generative Image Inpainting Network
A New Semantic-Guided Coarse-to-Fine Generative Image Inpainting Network
组织者
演讲者
时间
2025年04月24日 14:00 至 15:00
地点
A3-4-312
摘要
In this talk, a new semantic-guided coarse-to-fine generative image inpainting network (SCF-GMIN) is proposed to solve this challenging problem. The main idea is to construct a new two-stage conditional generative adversarial network to fill the missing regions involving multiple semantic categories by introducing external semantic information. Instead of directly inpainting the corrupted image, we leverage the semantic map (SM) inpainted in stage one to guide the inpainting process of corrupted regions in stage two. Both two stages share a coarse-to-fine network that enhances the visual quality of restorations. In our model, the proposed feature module plays a crucial role by storing semantic information of different categories in the feature space with the help of SM. The image inpainting tasks of semantic maps and corrupted images complement each other in the training process, which promotes a balance between understanding image semantics and maintaining visual consistency.
演讲者介绍
蔡云峰从2000年9月到2004年7月在中国科学技术大学数学系学习。2004年9月,他进入北京大学数学学院开始研究生的学习,并于2009年1月获得理学博士学位。在2009年1月到2012年6月期间,他先后在中科院数学所与美国加州大学戴维斯分校从事博士后工作。2012年9月,他加入北京大学数学学院,任特聘研究员。2018年9月他加入百度研究院,从事人工智能的研究工作。2024年6月,他加入BIMSA,获聘教授。