Xiao Wang | Computer Vision | Best Researcher Award

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Prof. Xiao Wang | Computer Vision | Best Researcher Award 

Xiao Wang is an associate professor at the School of Computer Science and Technology at Anhui University. He earned his bachelor degree from West Anhui University and a doctoral degree in computer science from Anhui University, with additional training as a visiting scholar at Sun Yat-sen University and the University of Sydney. He later conducted postdoctoral research at Pengcheng Laboratory in Shenzhen. His research focuses on computer vision, event-based vision, machine learning, and pattern recognition. He has contributed to advancing multi-modal learning and large-scale pre-trained models while serving as an associate editor and reviewer for leading international journals and conferences.

Prof. Xiao Wang | Anhui University | China

Profiles

ORCID

Education

  • Xiao Wang completed his bachelor studies at West Anhui University in Luan, China. He then pursued a doctoral degree in computer science at Anhui University in Hefei, China, under the guidance of Professors Jin Tang and Bin Luo as part of the Multi-Modal Intelligent Computing Group. During his doctoral studies, he broadened his academic exposure through visiting scholar positions at Sun Yat-sen University under Professor Liang Lin and at the University of Sydney in the UBTECH Sydney Artificial Intelligence Centre under Professor Dacheng Tao.

Experience

  • After his doctoral training, he worked as a postdoctoral researcher at Pengcheng Laboratory in Shenzhen, where he collaborated with Professors Feng Wu, Yonghong Tian, and Yaowei Wang. He currently serves as an associate professor at the School of Computer Science and Technology at Anhui University in Hefei. In addition to his teaching and research duties, he also acts as an associate editor for a leading IEEE journal and participates as a program committee member for top international conferences, while frequently serving as a reviewer for prestigious journals and conferences in computer vision and artificial intelligence.

Awards and Recognition

  • He is a recognized member of IEEE and has been entrusted with influential editorial, reviewing, and committee responsibilities. These roles demonstrate the recognition of his professional standing and the trust placed in him by the global academic community.

Skills and Expertise

  • Xiao Wang has strong expertise in event-based vision, deep learning, visual tracking, artificial intelligence, and representation learning. His skills extend beyond technical knowledge, as evidenced by his active editorial roles, peer-review commitments, and program committee services, reflecting his ability to guide and evaluate cutting-edge research.

Research Focus 

  • His research primarily concentrates on computer vision, event-based vision, machine learning, and pattern recognition. He has developed innovative methods for pedestrian attribute recognition across domains, enhanced by large language model frameworks. He has also contributed surveys and analyses on large-scale multi-modal pre-trained models, offering comprehensive insights into architectures, challenges, and future trends.

Publications

Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
Authors: Wang, Xiao; Chen, Guangyao; Qian, Guangwu; Gao, Pengcheng; Wei, Xiao-Yong; Wang, Yaowei; Tian, Yonghong; Gao, Wen
Journal: ArXiv

Tiny Object Tracking: A Large-Scale Dataset and a Baseline
Authors: Zhu, Yabin; Li, Chenglong; Liu, Yao; Wang, Xiao; Tang, Jin; Luo, Bin; Huang, Zhixiang
Journal: IEEE Transactions on Neural Networks and Learning Systems

Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification
Authors: Yuan, Chengguo; Jin, Yu; Wu, Zongzhen; Wei, Fanting; Wang, Yangzirui; Chen, Lan; Wang, Xiao
Journal: ArXiv

Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute Recognition
Authors: Zhu, Jun; Jin, Jiandong; Yang, Zihan; Wu, Xiaohao; Wang, Xiao
Journal: ArXiv

Pedestrian Attribute Recognition: A Survey
Authors: Wang, Xiao; Zheng, Shaofei; Yang, Rui; Zheng, Aihua; Chen, Zhe; Tang, Jin; Luo, Bin
Journal: ArXiv

Conclusion

  • Xiao Wang has steadily progressed from undergraduate studies to postdoctoral research and now holds a respected academic position at Anhui University. His journey highlights a balance of theoretical innovation and practical contributions to computer vision and artificial intelligence. With his leadership in multi-modal learning and strong engagement in the global research community, he continues to make significant impacts in the field.

Ciliang Shao | Computer Vision | Best Researcher Award

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Mr. Ciliang Shao | Computer Vision | Best Researcher Award 

Ciliang Shao is an undergraduate student of Computer Science and Technology at Sichuan University, Pittsburgh Institute. His research focuses on computer vision and pattern recognition, with contributions to projects on respiratory motion modeling, cross-modal MRI-TRUS registration, and three-dimensional scene generation. He has co-authored a publication, presented at academic conferences, and earned recognition on the Dean list. Through research projects and an internship at Zhejiang University, he has developed strong skills in deep learning, data processing, and medical imaging applications, reflecting his passion for innovation and commitment to advancing technology for real-world impact.

Mr. Ciliang Shao | Sichuan University | China

Profile

GOOGLE SCHOLAR

Education

  • Ciliang Shao is pursuing undergraduate studies in Computer Science and Technology at Sichuan University within the Pittsburgh Institute. His academic journey has been defined by a strong interest in computer vision and pattern recognition. During his studies, he has consistently demonstrated academic excellence, earning a place on the Dean list. His education has not only provided him with technical knowledge but has also nurtured his ability to engage in innovative research projects addressing real-world challenges

Experience

  • Ciliang Shao has gained valuable experience through participation in research projects and internships. He has contributed to studies on inter-subject lung respiratory motion modeling and cross-modal MRI-TRUS registration in prostate cancer, where he applied advanced frameworks such as E-CMCA and LSTM. In addition, he has worked on the generation of details for infinite three-dimensional scenes. His internship at Zhejiang University further strengthened his technical foundation and problem-solving abilities. These experiences have shaped his research outlook and deepened his commitment to advancing computer vision techniques.

Awards and Recognition

  • Recognition of his academic dedication came through inclusion in the Dean list, highlighting his consistent excellence and strong academic performance. His achievements in both coursework and research projects underscore his potential as a future researcher and innovator in his field.

Skills and Expertise

  • Ciliang Shao has developed strong skills in computer vision, pattern recognition, and deep learning techniques. He is proficient in designing and implementing frameworks for cross-modal data alignment and recognition. His ability to collaborate in group projects has enhanced his teamwork and communication skills, while his internship experience provided hands-on exposure to applying theoretical knowledge in practical settings. His research presentations and published work further reflect his ability to translate complex ideas into meaningful contributions.

Research Focus 

  • His research interests lie at the intersection of computer vision and pattern recognition. He focuses on developing innovative frameworks that enhance accuracy and efficiency in complex data processing tasks, particularly in medical imaging and healthcare applications. By exploring motion modeling and cross-modal data registration, his work aims to support more reliable diagnostic and treatment tools. He is also intrigued by the creative possibilities of generating detailed infinite three-dimensional scenes, expanding the scope of computer vision applications.

Publication

  • Inter-Subject Lung Respiratory Motion Modeling with Motion Artifacts Reduction
    Author: Ciliang Shao, Hejia Zhang, Jingjing He, Yang Ye, Kunpeng Wang
    Journal: Special session CIST

  • E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer
    Author: Ciliang Shao, Ruijin Xue and Lixu Gu.
    Journal: Journal of Imaging (MDPI)

Conclusion

  • Ciliang Shao has demonstrated remarkable research potential through his contributions to computer vision and pattern recognition at an early stage of his academic journey. His work on medical imaging and three-dimensional scene generation reflects both innovation and practical impact. With strong academic achievements, research publications, and recognition such as the Dean list, he is highly suitable for the Best Researcher Award. Continued expansion of collaborations and deeper engagement with global research networks will further strengthen his academic trajectory and establish him as a future leader in his field.

Mr. Xiang Zhang | Computer Vision | Best Researcher Award | 1159

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Mr. Xiang Zhang | Computer Vision| Best Researcher Award

Mr. Xiang Zhang , Northwest University, China

 Profile

Scopus

OrcID

📚 Early Academic Pursuits

Zhang Xiang’s journey in academia began with a keen interest in artificial intelligence and its applications. His undergraduate and graduate studies laid a strong foundation in computer science, with a focus on machine learning and computer vision. His early research was characterized by innovative approaches to image analysis and pattern recognition, which set the stage for his future contributions.

💼 Professional Endeavors

As a lecturer at Northwest University, Zhang Xiang has been dedicated to teaching and mentoring the next generation of computer scientists. His professional endeavors extend beyond the classroom, involving significant contributions to various research projects. He has successfully hosted a National Natural Science Foundation youth project and a horizontal project, showcasing his leadership and expertise in his field.

🔬CONTRIBUTIONS AND RESEARCH FOCUS

  • Zhang Xiang has made significant contributions to the field of computer vision and machine learning. His research primarily focuses on large-scale fine-grained image analysis, where he develops sophisticated algorithms to enhance image recognition accuracy and efficiency. He is also a pioneer in near-zero knowledge learning, innovating methods that enable AI systems to learn and adapt with minimal prior information. Additionally, Zhang Xiang has explored the potentials of AIGC (Artificial Intelligence Generated Content), pushing the boundaries of how AI can autonomously create and manipulate visual content. His work has not only advanced theoretical understanding but also led to practical applications in various domains, including autonomous driving, remote sensing, and digital signal processing. By authoring over 16 influential papers and securing multiple patents, Zhang Xiang has established himself as a leading figure in advancing computer vision technology.

🌟Impact and Influence

  • Zhang Xiang’s work has had a substantial impact on the field of artificial intelligence. His publications in top-tier journals and conferences have been widely cited, reflecting the significance and influence of his research. His contributions have advanced the state of the art in machine learning and computer vision, with practical applications in various domains.

 📖 ACADEMIC CITES

Zhang Xiang’s academic papers have garnered significant attention in the scientific community. His work in IEEE Transactions on Image Processing, IEEE Transactions on Geoscience and Remote Sensing, and AAAI has been particularly influential, contributing to the advancement of knowledge and technology in artificial intelligence.

🌍 LEGACY AND FUTURE CONTRIBUTIONS

Zhang Xiang’s legacy in the field of artificial intelligence is marked by his innovative research and dedication to education. Looking ahead, he aims to continue pushing the boundaries of AI research, with a focus on developing more efficient and intelligent systems. His future contributions are expected to further enhance the capabilities of AI technologies and their applications across various industries.

📰PUBLICATIONS

    • ConvSRGAN: Super-Resolution Inpainting of Traditional Chinese Paintings
      • Authors: Hu, Q., Peng, X., Li, T., Wang, J., Peng, J.
      • Journal: Heritage Science, 2024, 12(1), 176
    • DRANet: A Semantic Segmentation Network for Chinese Landscape Paintings
      • Authors: Hu, Q., Zhou, W., Peng, X., Peng, J., Fan, J.
      • Journal: Digital Signal Processing: A Review Journal, 2024, 147, 104427
    • Enabling Near-Zero Cost Object Detection in Remote Sensing Imagery via Progressive Self-Training
      • Authors: Zhang, X., Jiang, X., Hu, Q., Peng, J., Fan, J.
      • Journal: IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, 5628514
    • A Benchmark Dataset and Approach for Fine-Grained Visual Categorization in Complex Scenes
      • Authors: Zhang, X., Zhang, K., Zhao, W., Peng, J., Fan, J.
      • Journal: Digital Signal Processing: A Review Journal, 2023, 137, 104033
    • Boundary-Aware Bilateral Fusion Network for Cloud Detection
      • Authors: Zhao, C., Zhang, X., Kuang, N., Zhong, S., Fan, J.
      • Journal: IEEE Transactions on Geoscience and Remote Sensing, 2023, 61, 5403014