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Sept. 16, 2025, 6:34 a.m.
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| Zhou K. Large Vision-Language Models. Pre-training, Prompting, and Apps 2026.pdf | 70.7 MB |
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SOURCE: Zhou K. Large Vision-Language Models. Pre-training, Prompting, and Apps 2026
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COVER
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MEDIAINFO
Textbook in PDF format The rapid progress in the field of large multimodal foundation models, especially vision-language models, has dramatically transformed the landscape of Machine Learning, computer vision, and natural language processing (NLP). These powerful models, trained on vast amounts of multimodal data mixed with images and text, have demonstrated remarkable capabilities in tasks ranging from image classification and object detection to visual content generation and question answering. 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. It is an essential resource for researchers, practitioners, and students in the fields of computer vision, natural language processing, and Artificial Intelligence. 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. Foundations of Vision-Language Models: Concepts and Roadmap Part I Scaling Intelligence: Pre-Training Strategies for Vision-Language Models InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks Multimodal Large Language Models for Video Understanding Generative Multimodal Models Are In-Context Learners Part II Shaping Intelligence: Prompting Techniques for Multimodal Adaptation Differentiable Prompt Learning for Vision-Language Models Test-Time Prompt Tuning for Vision-Language Models Learning Efficient Feature Adapters for Vision-Language Models Efficient Tuning of Vision Foundation Models with Neural Prompt Search Confidence Calibration in Contrastive Vision-Language Models. Part III Applying Intelligence: Real-World Applications of Vision-Language Models Open-Vocabulary Object Detection Based on Detection Transformers Unlocking CLIP for Zero-Shot Dense Segmentation Adapting CLIP for 3D Understanding Multimodal Face Generation and Manipulation with Collaborative Diffusion Models Boosting Diffusion U-Net with Free Lunch for Text-to-Image and Text-to-Video Generation Text-Conditioned Zero-Shot 3D Avatar Creation and Animation Text-Driven 3D Human Motion Generation Text-Driven Scene Generation
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