Chapter 11 Preview: Multimodal Deep Learning: Intelligence Beyond Limits

Hello. In the soon-to-be-published Chapter 11, we will explore the forefront of multimodal deep learning and delve into the amazing capabilities of the latest models and their future prospects. Based on the content covered in Chapter 10, we have prepared more advanced topics and new examples.

In this chapter, we will go beyond simply fusing multiple modalities and embark on a journey towards creating systems that truly possess “multimodal intelligence”. In particular, we will thoroughly examine the following key topics:

• Practical example extensions: The Gemini extension example, which combines audio, images, and questions, and implementing an actual Large Multimodal Model (LMM) to perfectly understand the working principles of multimodal models.
• In-depth analysis of the latest models: Reflecting the latest model trends in 2025, we will closely examine the LMM architecture, simplify and implement LMM-based models based on CLIP ViT and LLaMA 2/Vicuna, and explore methods to improve model performance through Visual Instruction Tuning.
• Future prospects and challenges: Introducing the latest models such as Flamingo, Kosmos-2.5, GPT-4V, and Gemini Ultra 2.0, we will objectively compare their performance using multimodal benchmark datasets and evaluation metrics. We will also discuss the future of multimodal deep learning and its challenges, providing insights into the prospects after 2025 to inspire your research and development.

Chapter 11 is designed not only to cover theoretical content but also to allow you to directly build and experiment with multimodal models through actual code. Through this, you will be able to clearly understand the core concepts of multimodal deep learning and develop the ability to apply them in practice.

See you in the soon-to-be-released Chapter 11.