Congratulations to Ruan Liheng on his successful graduation!

Ruan Liheng graduated from Shanghai University with a bachelor’s degree and began his master’s degree in September 2022 at the School of Computer Engineering and Science, Shanghai University. After joining the research group, he studied shape space theory and image generation technologies and applications under the guidance of Professor Han Yuexing. Under Professor Han’s careful guidance, he completed the following research:

1. An image generation method based on the migration of information from pre-shaped spatial geodesic surfaces is proposed to address the challenges faced by image generation models when there are too few training samples and a lack of applicable pre-training models. The method overcomes the bottleneck of the model’s difficulty in effectively learning the distribution of very few samples and aims to generate high-quality and diverse images. The core process is as follows: firstly, the depth features of a small number of samples are extracted and used to construct a geodesic surface in pre-shape space for nonlinear feature enhancement; then, a pseudo-source domain is constructed based on the enhanced features to simulate a rich data distribution, and information migration from the pseudo-source domain to the target domain is carried out; finally, interpolated supervisory and regularisation constraints are imposed at the information migration stage for optimisation. Experiments demonstrate that, compared with existing methods, this method significantly improves the quality, detail richness and diversity of the generated images on multi-domain datasets, effectively mitigates pattern collapse, and demonstrates the potential of its generated images in assisting downstream tasks.

2. To address the challenges of the text-guided zero-sample image style migration task, a zero-sample style migration method based on the enhancement of geodesic surface features in the pre-shape space is proposed. The method aims to efficiently inject external novel style information into the pre-trained model while ensuring style consistency and content accuracy. Specifically, the method applies the idea of geodesic feature enhancement to the style migration framework based on the pre-trained diffusion model, combines with sliding window cropping to process the local information, and facilitates the effective fusion of the style and content features in the pre-shape space using the geodesic feature enhancement module. Experiments show that the method can achieve flexible text-guided style control without additional model fine-tuning or style references, and can better maintain the original content structure when generating images with the target style compared to the comparison model.

After graduation, Ruan Liheng will join Huawei. Looking back on his three years in Shanghai University, he has studied hard, conducted serious research, continued to improve his professional ability, and also formed a deep friendship with many teachers and friends. We hope that Ruan Liheng will take this experience and gain, not forgetting his original intention, and will ride the waves of the wind and have a bright future in the future.

Essay: Research and Application of Few-Shot Image Generation Method with Feature Augmentation Based on The Shape Space Theory

Code: https://github.com/P2i42/FAGStyle