Dejene Mengistu Sime, PhD

Postdoctoral Researcher
Academy of Advanced Interdisciplinary Research (AAIR) Guangzhou Institute of Technology (GIT) Xidian University, Xi’an, China

Email: djene.mengistu@gmail.com
GitHub: github.com/djene-mengistu

📝 Summary

Computer vision researcher with 6+ years of experience in machine learning and deep learning for industrial applications. Specialized in data-efficient learning (weakly supervised, semi-supervised, and contrastive learning) for defect detection, anomaly localization, and automated inspection systems. Passionate about bridging research and real-world industrial deployment.

🎓 Education

  • PhD in Mechanical Engineering (Computer Vision and Intelligent Equipment Track)
    University of Electronic Science and Technology of China (UESTC), Chengdu, China
    July 2023
    Dissertation: Deep Learning-based Machine Vision for Automated Defect Segmentation

  • MSc in Industrial Engineering
    Addis Ababa University, Addis Ababa Institute of Technology, Ethiopia
    July 2013

  • BSc in Industrial Engineering
    Bahir Dar University, Ethiopia
    June 2010

📚 Selected Publications

Journal Papers and Conferences

  1. Dejene M. Sime, Nan Ouyang, Kai Sheng, Getu T. Fellek, Wan Wenkang, Adnan A. Qaseem, Xiaojiang Ren, and Shehui Bu, “Saliency-Guided Transformer Attention with Pixel-Level Contrastive Learning for Weakly Supervised Defect Localization,” IEEE Transactions on Industrial Informatics, January, 2026.

  2. Dejene M. Sime, Guotai Wang, Zhi Zeng, Wei Wang, and Bei Peng, “Semi-Supervised Defect Segmentation with Pairwise Similarity Map Consistency and Ensemble-Based Cross-Pseudo Labels,” IEEE Transactions on Industrial Informatics, pp. 9535–9545, V.19-9, 2022.

  3. Dejene M. Sime, Guotai Wang, Zhi Zeng, and Bei Peng, “Uncertainty-aware and dynamically-mixed pseudo labels for semi-supervised defect segmentation,” Computers in Industry, pp.103995, V.152, 2023.

  4. Dejene M. Sime, Guotai Wang, Zhi Zeng, and Bei Peng, “Deep learning-based automated steel surface defect segmentation: a comparative experimental study” Multimedia Tools Applications, pp. 2995-3018, V.83, 2023.

  5. Wenkang Wan, Kai Sheng, Qing Cai, Lei Ao, Nan Ouyang, Haoxuan Feng & Dejene M. Sime, “Enhanced tire road friction coefficient estimation through interacting multiple model design based on tire force observation”, NonLinear Dynamics, January 2025.

  6. Adnan A. Qaseem, Lei Ao, Kai Sheng, Dejene M. Sime, Qing Cai, Jianzhao Li and Xiaojiang Ren, “DSTIFormer: Vehicle Trajectory Prediction Considering Dynamic Spatial-Temporal Interaction-aware with Transformer Network”, IEEE Transactions on Vehicular Technology, September 2025.

  7. Nan Ouyang, Xiaojiang Ren, Wenkang Wan, Dejene M. Sime, Qing Cai, Kai Sheng, “A Distributed Lightweight Edge Traffic Spatio-Temporal Forecasting Framework with Regional Autonomy and Global Feature Generation”, SSRN 6192959, ArXix, 2026.

  8. Dejene M. Sime, Guotai Wang, Zhi Zeng, and Bei Peng, “Semi-supervised Defect Segmentation with Uncertainty-aware Pseudo-labels from Multibranch Network”, In 5th International Conference on Image Processing and Machine Vision (IPMV) (IPMV 2023), January 13–15, 2023, Macau, China. ACM, New York, NY, USA.

  9. Daihan Wang, Yongyi Chen, XiaoJie Mao, Kai Sheng, Dejene M. Sime*, Balakrishnan Ramalingam, Chengsheng Miao and Shehui Bu, “Lightweight Design of YOLOv5s Object Detection Architecture for Roadside Edge Devices in Autonomous Driving” in 17th International Conference on Machine Learning and Computing (ICMLC 2025), Guangzhou, China on February 14-17, 2025.

  10. Adnan A. Qaseem, Kai Sheng, Dejene M. Sime*, Hifza Aurangzeb, Shehui Bu, Balakrishnan Ramalingam, “ST-GAN: Spatial-Temporal Graph Attention Network for Traffic Prediction” in the 10th International Conference on Computer and Communication Systems (ICCCS 2025), Chengdu, China on April 18-21, 2025.

  11. Adnan A. Qaseem, Kai Sheng, Dejene M. Sime*, and Eshetie B. Atanaw, “Spatial-Temporal Dynamic Features Fusion Graph-Transformer Network for Traffic Flow Prediction”, in the 5th Asia Conference on Algorithms, Computing and Machine Learning (CACML2026), March 27 – 29, 2026, Guangzhou, China.

  12. Wei Jin, Qifeng Xie, Wenkang Wan, Lei Ao, Dejene M. Sime, Kai Sheng, “A comparative study of DRL-based autonomous driving under single-vehicle and human-vehicle-road coordination”, In International Conference on Automation, Robotics and Computer Engineering (ICARCE 2023), December 14–16, 2023, Wuhan, China.
    (Full publication list available upon request)

💼 Research & Work Experience

Postdoctoral Researcher (Sep 2023 – Present)
Xidian University, Xi’an, China

  • Leading research on weakly supervised defect detection and industrial machine vision
  • Developing scalable AI systems for intelligent inspection and anomaly detection
  • Mentoring graduate students and collaborating on national/provincial research projects

Lecturer (2013 – 2019)
Adama Science and Technology University & Mekelle University, Ethiopia

  • Taught courses in Operations Research, Optimization, and CAD-CAM
  • Mentored over 60 undergraduate students on final-year theses

🔬 Research Interests

  • Vision-Language Models for Industrial Applications
  • Scalable AI Deployment in Smart Manufacturing
  • Intelligent/smart manufacturing solutions
  • Intelligent equipment and robotics
  • Machine vision and deep learning applications in industry
  • Semi-supervised and unsupervised deep learning methods
  • Vision-Language Foundation Models
  • LLM and agentic modeling for industrial solutions
  • Industrial robotic perception and visual control
  • Automated industrial inspection, anomaly detection, pattern recognition, and fault diagnosis
  • Digital manufacturing, human-machine collaboration
  • Intelligent machine fault diagnosis and prediction
  • Intelligent transportations, Vision based guidance (AGVs)
  • Medical image segmentation and analysis
  • Computational optimization and system analysis

🛠️ Skills

Programming & Tools
Python, PyTorch, TensorFlow, OpenCV, LaTeX, GAMS, LINGO

Core Expertise
Computer Vision, Deep Learning, Semantic Segmentation, Weakly Supervised Learning, Transformer Architectures, Industrial Machine Vision

Languages

  • English: Professional (fluent)
  • Amharic & Afan Oromo: Native
  • Tigrigna & Chinese: Beginner

📬 Contact

Last updated: April 2026