LLMs have transformed text processing in AI and machine learning. Now, Large Vision Models (LVMs) are emerging, set to similarly revolutionize image processing and interpretation.
Today I'll cover:
- What are Large Vision Models?
- LVMs vs. LLMs: A Key Distinction
- Examples of LVMs
- The Internet's Bias: A Challenge for LVMs
- Domain-specific LVMs
- Industry Use Cases of Domain-Specific LVM use cases
- The Future of LVMs: A Revolution in Sight
Let’s Dive In! 🤿
What are Large Vision Models?
Similar to how LLMs leverage vast text corpuses to learn linguistic patterns, LVMs ingest massive image datasets to recognize visual concepts and features. Popular examples include Google's Imagen, and Stability AI's Stable Diffusion.
LVMs encompass 100s of millions of parameters, allowing them to generate amazingly realistic synthetic images, and caption photographs, classify over 37,000 image categories, and much more. Their flexibility even lets them interpret X-ray scans, satellite imagery, and microscope photos.
The potential applications are vast. For instance, combining LVMs with LLMs could enable precise analysis in medical fields, like counting cancer cells in human tissues and predicting disease progression.
However, internet images make up most training data for current models. So while they excel at tasks related to pets, travel destinations, and everyday objects, LVMs struggle with niche image domains.
LVMs vs. LLMs: A Key Distinction
While LVMs share a conceptual lineage with LLMs, there's a crucial distinction in their application and effectiveness. LLMs have shown remarkable prowess in understanding and generating text by training on vast quantities of internet-based text data. This success hinges on a crucial fact: internet text is sufficiently similar to proprietary documents, allowing LLMs to adapt and understand a wide range of textual content effectively.
Examples of LVMs
Let’s see some examples of LVMs and their capabilities:
CLIP (Contrastive Language-Image Pretraining): Developed by OpenAI, CLIP is a vision-language model that's trained to understand images in conjunction with natural language. It can be used for tasks such as image captioning, visual question answering, and image retrieval. Here’s the paper: link
Google's Vision Transformer (ViT): Also called ViT, this model is designed for image classification and employs a Transformer-like architecture over patches of the image. It has achieved state-of-the-art results on a variety of image classification benchmarks. Here’s the link to the paper: link
LandingLens™: This is a platform developed by LandingAI that allows users to create custom computer vision projects without any prior coding experience. It provides an intuitive interface for labeling images, training models, and deploying them to the cloud or edge devices.
The Internet's Bias: A Challenge for LVMs
There's a critical difference between LVMs and LLMs. While online text exhibits a relatively consistent structure and vocabulary, internet images are a diverse and heterogeneous collection. This presents a challenge for LVMs trained solely on generic internet images.
Imagine an LVM trained on millions of Instagram photos. These images are likely to be dominated by people, pets, landmarks, and everyday objects. While useful for general image recognition tasks, this exposure may not translate well to specialized applications.
Such an LVM might struggle to identify the subtle nuances between medical images or recognize minute defects in manufacturing equipment. This is where domain-specific LVMs play a crucial role.
Specialized Domains and Their Needs
Industries such as manufacturing, aerial imagery, life sciences, and others utilize images that bear little resemblance to the typical internet image. For example, in manufacturing, images might be of machinery parts, in aerial imagery, it could be geographical landscapes, and in life sciences, microscopic images of cells or tissues. In these domains, the most salient features of images differ significantly from those in internet images.
Industry Use Cases of Domain-Specific LVM use cases
- Diagnostic imaging: LVMs can analyze medical scans like X-rays, CT scans, and MRIs to detect abnormalities with high accuracy, aiding in early diagnosis and treatment.
- Virtual assistants for surgery: LVMs can be used to develop surgical robots that can perform complex procedures with minimal human intervention.
2. Retail and E-commerce:
- Product recommendation: LVMs can analyze images of products to recommend similar items to customers, improving the shopping experience and increasing sales.
- Visual search: Customers can use their smartphones to take photos of products they're interested in, and LVMs can be used to find similar products online.
3. Manufacturing and Industry:
- Quality control: LVMs can be used to inspect products for defects, ensuring that only high-quality products are shipped to customers.
- Predictive maintenance: LVMs can analyze images of machinery to predict when it is likely to fail, allowing for preventive maintenance to be performed before production is interrupted.
4. Security and Surveillance:
- Object detection: LVMs can be used to detect objects such as weapons and explosives in security cameras, helping to prevent attacks and ensure the safety of people and property.
- Facial recognition: LVMs can be used to identify individuals in security cameras, which can be used for law enforcement purposes or to identify authorized personnel.
- Crop health monitoring: LVMs can be used to analyze images of crops to detect diseases and pests, allowing farmers to take corrective action before their crops are damaged.
- Weed control: LVMs can be used to identify weeds in fields, allowing farmers to target them with herbicides more effectively.
- Special effects: LVMs are being used to create more realistic and believable special effects in movies and video games.
- Virtual reality: LVMs can be used to create more immersive virtual reality experiences.
- Image-based learning: Students can learn about different topics by interacting with images and having LVMs answer their questions about them.
- Accessibility: LVMs can be used to develop tools that help blind and visually impaired people access information that is otherwise inaccessible to them.
- Self-driving cars: LVMs are essential for the development of self-driving cars, as they allow the cars to understand their surroundings and navigate safely.
- Traffic management: LVMs can be used to improve traffic flow by analyzing traffic patterns and making real-time adjustments to traffic signals.
The Future of LVMs: A Revolution in Sight
The development of LVMs is still in its early stages, but the potential is immense. As these models continue to evolve and become more specialized, they have the power to transform a wide range of industries.
Foundation models have limitations such as high GPU requirements and slow real-time processing, making them unsuitable for edge deployment and cost-effective use. Their broad knowledge base often exceeds the needs of specialized AI applications. Recent advances in knowledge and dataset distillation offer a solution, allowing the transfer of large model capabilities to smaller, more practical models for specific real-world applications.
In the coming years, we can expect LVMs to become an essential tool for:
- Scientists and Researchers: LVMs will accelerate research and discovery across various scientific fields.
- Engineers and Manufacturers: LVMs will improve product quality, optimize production processes, and reduce costs.
- Healthcare Professionals: LVMs will enhance diagnostic accuracy, personalize treatment plans, and improve patient outcomes.
- Environmental Scientists: LVMs will provide deeper insights into environmental issues and enable more effective conservation efforts.
- And Many More: The potential applications of LVMs are endless, reaching far beyond these initial examples.
The LVM revolution is upon us, and it's time to embrace the transformative power of these models in shaping a better future.