Mastering New Age Computer Vision

Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications.

This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks.

By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field.

KEY FEATURES

* Master PyTorch for image processing, segmentation, and object detection.
* Explore advanced computer vision techniques like ViT and panoptic models.
* Apply multi-tasking, metric, bilinear pooling, and self-supervised learning in real-world scenarios.

WHAT YOU WILL LEARN

* Use PyTorch for both basic and advanced image processing.
* Build object detection models using CNNs and modern frameworks.
* Apply multi-task and multi-instance learning to complex datasets.
* Develop segmentation models, including panoptic segmentation.
* Improve feature representation with metric learning and bilinear pooling.
* Explore transformers and self-supervised learning for computer vision.

WHO THIS BOOK IS FOR

This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed.

1. Evolution of New Age Computer Vision Models
2. Image Processing with PyTorch
3. Designing of Advanced Computer Vision Techniques
4. Designing Superior Computer Vision Techniques
5. Advanced Object Detection with FPN, RPN, and DetectoRS
6. Multi-instance Learning
7. More Advanced Multi-instance Learning
8. Beyond Classical Segmentation Panoptic Segmentation with SAM
9. Crafting Deep Metric Learning in Embedding Space
10. Navigating the Realm of Metric Learning
11. Multi-tasking with Multi-task Learning
12. Fine-grained Bilinear CNN
13. The Rise of Self-supervised Learning
14. Advancements in Computer Vision La

Zonunfeli Ralte, known as Feli, is an accomplished AI leader with an extraordinary career spanning data science, artificial intelligence, and generative AI. With a Master's in Business Administration and Economics, and 16 years of professional experience across data science, analytics, finance, and AI, she has established herself as a trailblazer in her field. Currently, Feli serves as the CEO and Founder of RastrAI while also excelling as a Principal AI Consultant, crafting cutting-edge GenAI applications for diverse industries.

Feli’s illustrious journey is marked by significant achievements, including founding Northeast India’s first AI-focused company—a venture recognized by MasterCard AI Garage. Her entrepreneurial and technical prowess has consistently driven transformative innovation in emerging markets. Her dedication to advancing AI is underscored by an impressive portfolio of nine research papers, published in esteemed venues like IEEE and Springer, four of which have earned prestigious Best Paper awards, including acceptance by IIT Hyderabad and NIT Mizoram. She also holds a patent in Responsible AI and has authored the highly regarded book, Learn Python Generative AI: Journey from Autoencoders to Transformers to Large Language Models with BPB publications.

Through her pioneering research, influential publications, groundbreaking industry experience, and a persistent commitment to excellence, Zonunfeli has significantly advanced the field of AI and inspired professionals worldwide. Her ability to blend technical depth with practical innovation positions her as a visionary leader shaping the future of artificial intelligence.