The Evolution from ViT to CLIP to Multimodal Models: Exploring the Future of Vision-Language Integration
Understanding VLMs Through the Foundations of LLMs
I like to understand new technologies using a high-level yet first-principles approach. This helps me trace back to the fundamental building blocks, layer by layer, until I grasp why things have evolved the way they have. From Transformers to language models and now to multimodal models, they all share a common foundation. As demands evolve, engineering details are adjusted and optimized at different levels. Even if current models are not perfect, they lay a solid groundwork for future advancements.
This article is not about technical deep dives but rather about understanding the context and evolution of these technologies. It won't cover everything, but it will hold onto the core thread. Confidence fuels curiosity—let’s dive in!
Level: 101
Requirement: [1706.03762] Attention Is All You Need
Introduction
The integration of vision and language is driving a new wave of advancements in artificial intelligence. This article explores the evolution from ViT to CLIP to the latest multimodal models, highlighting how these technologies interact and propel AI forward in multimodal understanding and generation.
1. ViT: The Birth of Vision Transformers
For years, Convolutional Neural Networks (CNNs) dominated the field of computer vision. However, ViT introduced a fresh perspective. Instead of relying on convolutions, ViT segments an image into fixed-size patches and treats them as sequential data, much like tokens in natural language processing. Using a Transformer-based architecture, ViT captures long-range dependencies within images, achieving impressive performance on large-scale datasets.
Reference: [2010.11929] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
2. CLIP: Bridging Vision and Language
As interest in multimodal learning grew, OpenAI introduced CLIP, a model designed to process images and text simultaneously. By employing contrastive learning, CLIP aligns images and textual descriptions in the same embedding space, enabling it to understand their relationships. This allows CLIP to perform tasks like zero-shot classification, demonstrating remarkable generalization capabilities.
Reference: [2103.00020] Learning Transferable Visual Models From Natural Language Supervision
3. The Evolution of Multimodal Models: SmolVLM, OmniVision, DeepSeek-VL2
Building upon CLIP, researchers have developed more advanced multimodal models to enhance AI's ability to process and understand vision and language together. Here are some notable examples:
SmolVLM: A lightweight multimodal model optimized for efficiency in resource-constrained environments.
OmniVision: Aims for comprehensive visual understanding by integrating multiple sensory modalities.
DeepSeek-VL2: Leverages deep learning to create stronger connections between vision and language, improving reasoning capabilities.
Conclusion
From ViT’s architectural innovations to CLIP’s multimodal alignment and the latest advancements in vision-language models, AI has made significant strides in integrating vision and language. These developments expand AI’s applications and set the stage for future research and breakthroughs.