Congratulations to Chi Ruting on her successful graduation!
Chi Ruting graduated with a bachelor’s degree from Shanghai University and began pursuing an academic master’s degree in the School of Computer Engineering and Science at Shanghai University in 2021. Since her senior year of undergraduate studies, student Chi Yuting has been studying and researching image processing related technologies and applications in the Han Yuexing research group. Under the careful guidance of Professor Han, she has continued and advanced the following research:
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To address the issues of small sample size and complex microstructure features in material image semantic segmentation, a dual branch semantic segmentation network based on feature pyramid and cross attention is proposed. This network is divided into main branch and auxiliary branch. The main branch uses a feature pyramid model to aggregate multi-level image features to enhance detail information; The auxiliary branch uses low-level features of the backbone network to segment images, and the auxiliary network learns texture and boundary information. Under the collaboration of multi task supervision and multi-scale features, this method achieves the best performance on multiple small sample material image datasets compared to the comparative model.
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To address the significant issue of small sample size in material image instance segmentation, a instance segmentation method based on multimodal fusion and pseudo labeling technology is proposed to improve the utilization of existing data. This method improves the accuracy of object classification, localization, and segmentation by fusing multimodal data of images and text. This paper divides training into two stages: fully supervised training and semi supervised training. During the semi supervised training phase, pseudo labeling techniques are used to involve unlabeled data in the optimization of the supervised model. Under the collaboration of multimodal information and two-stage training, compared to the comparison model, this method achieves superior performance in various small sample instance segmentation scenarios.
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Design statistical analysis methods for the microscopic images of 2205 duplex stainless steel to explore the intrinsic relationship between material image segmentation results and material properties. This paper is based on semantic segmentation results and instance segmentation results, using material empirical formulas to efficiently and accurately obtain the growth rate and material performance changes of microstructures that are similar to manual analysis results.
After graduation, Chi Yuting joined Xiaomi to work on camera algorithms. During her three-year graduate studies at Shanghai University, student Chi Yuting worked hard and conducted research diligently, constantly enhancing her professional knowledge. She was fortunate enough to have met many good teachers and friends. I hope that student Chi Yuting will not forget her original intention, keep in mind her mission, overcome obstacles, and forge ahead on the road ahead.
