文献类型：会议论文

英文题名：Semantic-Aware Generator and Low-level Feature Augmentation for Few-shot Image Generation

作者：Wang, Zhe[1];Guan, Jiaoyan[1];Yang, Mengping[1];Xiao, Ting[2];Chi, Ziqiu[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Key Lab Smart Mfg Energy Chem Proc, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：31st ACM International Conference on Multimedia (MM)

会议日期：OCT 29-NOV 03, 2023

会议地点：Ottawa, CANADA

语种：英文

外文关键词：few-shot image generation; generative adversarial network; generator features

摘要：Few-shot image generation aims to generate novel images for an unseen category with only a few samples. Prior studies fail to produce novel images with desirable diversity and fidelity. To ameliorate the generation quality, we in this paper propose a Semantic-Aware Generator (SAG) to provide explicit semantic guidance to the discriminator, and a Low-level Feature Augmentation (LFA) technique to provide fine-grained information, facilitating the diversity. Specifically, we observe that the generator feature layers contain different levels of semantic information. Such observation motivates us to employ intermediate feature maps of the generator as semantic labels to guide the discriminator, improving the semantic awareness of the generator. Moreover, spatially informative and diverse features obtained via LFA contribute to better generation quality. Together with the aforementioned module, we conduct extensive experiments on three representative benchmarks and the results demonstrate the effectiveness and advancement of our method.