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Advancing Vision-Language Models with Generative AI

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Rahul Raja
Arpita Vats
Published in
ICT for Global Innovations and Solutions
Publisher
Springer Nature Switzerland

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Abstract

The chapter delves into the recent advancements in vision-language models (VLMs), highlighting the integration of large language models (LLMs) with image encoders to process multimodal data. It explores various models like CLIP, Flamingo, and BLIP, each with unique approaches to multimodal learning. The text discusses the challenges in training these models, including data limitations, computational complexity, and biases. It also covers future directions, such as improving video-based models and developing more robust evaluation methods. The chapter concludes by emphasizing the need for high-quality datasets and better grounding to enhance model performance and reliability.
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next chapter Aërlink: Blockchain-Enabled Supply-Chain Transparency for Counterfeit Prevention
Title
Advancing Vision-Language Models with Generative AI
Authors
Rahul Raja
Arpita Vats
Copyright Year
2026
Book
ICT for Global Innovations and Solutions

Print ISBN: 978-3-032-02852-5

Electronic ISBN: 978-3-032-02853-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-02853-2_1
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