Large Vision-Language Models

Pre-training, Prompting, and Applications. Edited by Kaiyang Zhou, Ziwei Liu and Peng Gao.

This book provides a comprehensive and up-to-date exploration of large vision-language models, covering the key aspects of their pre-training, prompting techniques, and diverse real-world computer vision applications. Large Vision-Language Models begins by exploring the fundamentals of large vision-language models, covering architectural designs, training techniques, and dataset construction methods. It then examines prompting strategies and other adaptation methods, demonstrating how these models can be effectively fine-tuned to address a wide range of downstream tasks. The final section focuses on the application of vision-language models across various domains, including open-vocabulary object detection, 3D point cloud processing, and text-driven visual content generation and manipulation. Beyond the technical foundations, the book explores the wide-ranging applications of vision-language models (VLMs), from enhancing image recognition systems to enabling sophisticated visual content generation and facilitating more natural human-machine interactions. It also addresses key challenges in the field, such as feature alignment, scalability, data requirements, and evaluation metrics. By providing a comprehensive roadmap for both newcomers and experts, this book serves as a valuable resource for understanding the current landscape, limitations, and future directions of VLMs, ultimately contributing to the advancement of artificial intelligence. XVII,429 Seiten mit zahlreichen Farbabb. und einigen Tab., gebunden (Advances in Computer Vision and Pattern Recognition/Springer Verlag 2026 [sic! recte: 2025]) leichte Lagerspuren/minor shelfwear