Attentive normalization for conditional image generation


Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain. It is still not sufficient for categories with complicated structures. In this paper, we characterize long-range dependence with attentive normalization (AN), which is an extension to traditional instance normalization. Specifically, the input feature map is softly divided into several regions based on its internal semantic similarity, which are respectively normalized. It enhances consistency between distant regions with semantic correspondence. Compared with self-attention GAN, our attentive normalization does not need to measure the correlation of all locations, and thus can be directly applied to large-size feature maps without much computational burden. Extensive experiments on class-conditional image generation and semantic inpainting verify the efficacy of our proposed module.

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (Oral, Acceptance Rate: 5.7%)
Ying-Cong Chen
Ying-Cong Chen
Assistant Professor

Ying-Cong Chen is an Assistant Professor at AI Thrust, Information Hub of Hong Kong University of Science and Technology (Guangzhou Campus). He obtained his Ph.D. degree from the Chinese University of Hong Kong. His research lies in the broad area of computer vision and machine learning, aiming for empowering machine with the capacity to understand human appearance, physiology and psychology. His works contribute to a wide range of applications, including contactless health monitoring, semantic photo synthesis, and intelligent video surveillance.