Xin Hu | Smart Education | Best Researcher Award

Published on by China Scientist

Dr. Xin Hu | Smart Education | Best Researcher Award

Xin Hu is a Lecturer at the School of Data Science and Artificial Intelligence, Chang’an University. Her research focuses on computer vision, few-shot learning, and multimodal learning. She earned her Ph.D. in Computer Science and Technology from Xi’an Jiaotong University, where she contributed to national key research projects in educational data analysis and knowledge engineering. Xin has published in top journals such as IEEE Transactions on Image Processing, Neural Computation, and AAAI, with notable work on few-shot diagram understanding and cross-modal attention models.

Dr. Xin Hu | Chang’an University | China

Profile

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Education

  • Xin Hu has a strong educational foundation rooted in computer science and technology. She completed her doctoral studies at Xi’an Jiaotong University in Computer Science and Technology under the supervision of Professor Jun Liu. Before that, she earned her Master of Engineering in Computer Technology and her Bachelor’s degree in Digital Media Technology from Xi’an Shiyou University, mentored by Professor Hongtao Hu. This academic trajectory reflects her deep engagement with both theoretical and applied aspects of computer science.

Experience

  • Xin Hu currently serves as a Lecturer at the School of Data Science and Artificial Intelligence, Chang’an University. Her professional journey began with project work related to semantic retrieval at Xi’an Shiyou University, which evolved into more advanced research during her doctoral studies. Her core research experience includes significant contributions to few-shot learning, object detection, and multimodal learning. She has led and contributed to several national key research projects, including those related to educational data analysis, big data knowledge engineering, and cross-modal retrieval. Her work has focused on solving real-world challenges such as diagram-sentence matching and fine-grained image classification.

Awards and Recognition

  • Xin Hu has made notable contributions to academic conferences and journals. Her research has been published in prestigious venues such as Neural Computation, IEEE Transactions on Image Processing, AAAI Conference on Artificial Intelligence, and IJCAI. These accomplishments reflect the quality and impact of her research in few-shot and multimodal learning. Her collaboration with interdisciplinary teams and leadership in project development have also established her as a promising scholar in artificial intelligence and computer vision.

Skills and Certifications

  • Xin Hu is proficient in machine learning, deep learning, and cross-modal attention modeling. She has hands-on experience with end-to-end attention-based models, anchor-free object detection frameworks, and multimodal learning strategies. Technically, she is skilled in backend and frontend development frameworks such as Spring Boot and React. Her analytical skills are complemented by her ability to guide and mentor junior researchers in complex research settings.

Research Focus

  • Xin Hu’s primary research interests lie at the intersection of computer vision, few/one-shot learning, and multimodal learning. She is particularly focused on educational applications of AI, such as diagram understanding and knowledge graph visualization. Her work explores how to make intelligent systems capable of recognizing, detecting, and relating uncommon visual and textual content from limited data. This includes the development of innovative models like the cross-modal attention graph model (Fs-DSM) and gestalt-perception-based transformers for diagram interpretation. Her research is aimed at bridging the gap between human-level perception and machine understanding in data-scarce scenarios.

Conclusion

  • Xin Hu exemplifies a new generation of researchers working at the frontier of artificial intelligence and education technology. With a firm grounding in computer vision and few-shot learning, her work addresses pressing problems in multimodal understanding and educational intelligence. Her blend of technical skill, academic rigor, and leadership makes her a valuable contributor to the research community, particularly in areas requiring innovative solutions for complex and underexplored data challenges.

Publications

  • LFSRM: Few-Shot Diagram-Sentence Matching via Local-Feedback Self-Regulating Memory
    Authors: Lingling Zhang, Wenjun Wu, Jun Liu, Xiaojun Chang, Xin Hu, Yuhui Zheng, Yaqiang Wu, Qinghua Zheng
    Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

  • Hierarchy-Based Diagram-Sentence Matching on Dual-Modal Graphs
    Authors: Wenjun Wu, Lingling Zhang, Jun Liu, Ming Ren, Xin Hu, Jiaxin Wang, Qianying Wang
    Journal: Pattern Recognition

  • SKFormer: Diagram Captioning via Self-Knowledge Enhanced Multi-Modal Transformer
    Authors: Xin Hu, Jiaxin Wang, Tao Gao
    Journal: Signal Processing

  • Alignment Relation is What You Need for Diagram Parsing
    Authors: Xinyu Zhang, Lingling Zhang, Xin Hu, Jun Liu, Shaowei Wang, Qianying Wang
    Journal: IEEE Transactions on Image Processing

  • Contrastive Graph Representations for Logical Formulas Embedding
    Authors: Qika Lin, Jun Liu, Lingling Zhang, Yudai Pan, Xin Hu, Fangzhi Xu, Hongwei Zeng
    Journal: IEEE Transactions on Knowledge and Data Engineering