文献类型：期刊文献

英文题名：Improving Few-shot Image Generation by Structural Discrimination and Textural Modulation

作者：Yang, Mengping[1]; Wang, Zhe[1]; Feng, Wenyi[1]; Zhang, Qian[2]; Xiao, Ting[2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230318921)

语种：英文

外文关键词：Semantics

摘要：Few-shot image generation, which aims to produce plausible and diverse images for one category given a few images from this category, has drawn extensive attention. Existing approaches either globally interpolate different images or fuse local representations with predefined coefficients. However, such an intuitive combination of images/features only exploits the most relevant information for generation, leading to poor diversity and coarse-grained semantic fusion. To remedy this, this paper proposes a novel textural modulation (TexMod) mechanism to inject external semantic signals into internal local representations. Parameterized by the feedback from the discriminator, our TexMod enables more fined-grained semantic injection while maintaining the synthesis fidelity. Moreover, a global structural discriminator (StructD) is developed to explicitly guide the model to generate images with reasonable layout and outline. Furthermore, the frequency awareness of the model is reinforced by encouraging the model to distinguish frequency signals. Together with these techniques, we build a novel and effective model for few-shot image generation. The effectiveness of our model is identified by extensive experiments on three popular datasets and various settings. Besides achieving state-of-the-art synthesis performance on these datasets, our proposed techniques could be seamlessly integrated into existing models for a further performance boost. Our code and models are available at here. ? 2023, CC BY-SA.