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This book proposes practical application paradigms for deep neural networks, aiming to establish best practices for their real-world implementation.
Table des matières
1. Introduction to Artificial Intelligence for Practical Application 2. Fine-grained Dataset Design Paradigm for Mask-Wearing Recognition 3. Hand-Held Action Detection Paradigm for Smoking Detection 4. Human Action Recognition Paradigm for Person Safety Supervision in Various Practical Scenarios 5. Person Counting Paradigm for Intelligent Video Surveillance in Various Practical Scenarios 6. Operation Procedure Detection Paradigm for Noncompliant Operations Detection of Oil Unloading 7. Object Measurement Paradigm for Length Measurement of Iron Chains 8. Quality Estimation Paradigm for Copper Scrap Granules Recycling 9. Human-in-the-Loop Learning Paradigm for Fabric Anomaly Detection 10. Supervised Learning Paradigm for Edible Oil Anomaly Detection 11. Unsupervised Learning Paradigm for Industrial Visual Anomaly Detection 12. Object Identity Recognition Paradigm for Fishing Boat Recognition 13. Image Editing Paradigm for Clothing Fashion Customization 14. Retrieval-Augmented Generation Paradigm for Professional Knowledge Acquisition Applications 15. Multimodal Generation Paradigm for Visualizing Historical Artifacts
A propos de l'auteur
Shiguo Lian received his Ph.D. from Nanjing University of Science and Technology, China. He currently serves as Chief Scientist at the Data Science & Artificial Intelligence Research Institute and Chief Engineer of AI Technology, China Unicom. He is a member of the IEEE Multimedia Communications and Computational Intelligence Technical Committees. His research focuses on visual recognition, multimodal large models, robotics, and multimodal interactions.
Zhaoxiang Liu received his Ph.D. from the College of Information and Electrical Engineering at China Agricultural University, China. He currently serves as Director of AI Research at the Data Science & Artificial Intelligence Research Institute, China Unicom. His research interests include artificial intelligence, large language models, multimodal large models, deep learning, computer vision, and embodied AI.
