Large Vision-Language Models: Pre-training, Prompting, and Applications by Kaiyang Zhou, Ziwei Liu & Peng Gao

Large Vision-Language Models: Pre-training, Prompting, and Applications by Kaiyang Zhou, Ziwei Liu & Peng Gao

The guiding principle of Large Vision-Language Models is that the next generation of artificial intelligence must seamlessly bridge the gap between visual perception and textual comprehension to achieve true open-world utility. The editors address a monumental paradigm shift in artificial intelligence: the transition from isolated, single-task deep learning architectures (like discrete image classifiers or basic text tokens) to multimodal foundation models. While early machine learning required entirely separate pipelines to label an image and write a caption, modern VLMs utilize shared, aligned high-dimensional vector spaces to read both pixels and words natively and simultaneously.

Rather than offering simple, surface-level API usage guides, this textbook approaches VLMs from first-principles infrastructure engineering. It uncovers the core algorithmic mechanics of historical models like CLIP and ALIGN before breaking down modern transformer configurations. Across its deeply technical curriculum, the text explores structural dataset engineering, cross-modality token fusion, and the math governing scaling laws. By anchoring these profound engineering formulas next to specialized applications—such as open-vocabulary object detection, 3D point cloud semantics, and text-guided visual generation—the book serves as a technical compass for researchers and advanced system designers pushing the boundaries of spatial intelligence.

As regional artificial intelligence research labs, graduate engineering departments, and deep-tech enterprise engineering centers work to build next-generation autonomous systems, robotics, and medical imaging software, they are hitting a critical technical wall. While general developers can easily implement basic text-only models, building systems that truly understand the real world requires master-level competence in multimodal engineering—leaving many teams trapped behind fragile, unaligned image pipelines and inefficient training routines.

Large Vision-Language Models provides the ultimate, rigorous technical compass today's advanced researchers require. Kaiyang Zhou, Ziwei Liu, and Peng Gao flawlessly combine their world-class academic leadership with direct, mathematically precise instruction. By filling every single chapter with dense architectural breakdowns, scaling law proofs, and concrete data-engineering lifecycles, this Springer volume equips machine learning scientists, computer vision engineers, and graduate computer science students with the exact foundational tools needed to construct resilient, cutting-edge multimodal applications. It is an indispensable cornerstone text for any serious artificial intelligence library.

Language: English.

Genre: Deep Learning Infrastructure

Binding: সেলাই করা বাইন্ডিং

Quality: Premium Quality Books.

Printing: High Quality Printing.

Paper: Eye Friendly paper (Cream White)

Cover: Matt cover (Paperback).