After the Success of LLMs, Get Ready for Large Vision Models (LVMs)

Imagine browsing a website that sells clothes, furniture, or cars.

You see a product that attracts you, and you want to know more — so you click on it and are greeted with a fantastic image showing every product detail and feature.

You can zoom in, rotate, change the product’s color, and see its appearance in different settings and scenarios.

Dazzled by what you see, you decide to buy the product. And e-commerce has another satisfied customer.

Now, imagine that the image you saw was not an actual photograph but a synthetic one created by an artificial intelligence (AI). The product you bought may not even exist in the physical world but only in the digital one.

This is the way online shopping is moving. AI models that can process and interpret visual data, such as images or videos, are becoming more advanced and powerful, enabling new and better applications and experiences across various domains and industries.

These models are called Large Vision Models (LVMs), similar to Large Language Models (LLMs).

However, LVMs focus on the visual domain and can perform various tasks related to computer vision, such as image classification, object detection, face recognition, semantic segmentation, image generation, and more.

Table of Contents Table of Contents

1. Key Takeaways
2. The State of LVMs Today
3. Use Cases of LVMs
4. The Challenges of LVMs
5. The Bottom Line

Show Full Guide

Table of Contents

1. Key Takeaways
2. The State of LVMs Today
3. Use Cases of LVMs
Show Full Guide
4. The Challenges of LVMs
5. The Bottom Line

LVMs are trained on large and diverse datasets of images or videos using advanced neural network architectures, such as Convolutional Neural Networks (CNNs) or transformers. In addition, LVMs can combine vision and language modalities, enabling tasks such as image captioning, visual question answering, and image retrieval.

For example, image captioning generates a natural language description of an image, such as “A man mowing a lawn on a sunny day.” Similarly, in visual question-answering scenarios, LVMs can skillfully respond to natural language queries about images, such as “What color is the lawn mower?”

The State of LVMs Today

The underlying mechanism of LVMs involves encoding input visual data into a high-dimensional vector representation. Subsequently, LVMs use this representation to generate an output, such as a label, a caption, or a new image.

Additionally, LVMs leverage these representations for comparison with other data, such as textual queries, enabling them to match and retrieve relevant information effectively.

For instance, OpenAI’s CLIP, an LVM, learns visual concepts from natural language queries. Meta AI’s DINOv2 excels in features like depth estimation, while Ultralytics’ YOLOv8 demonstrates high accuracy in detecting objects while being a relatively compact model.

LVMs have adaptability through transfer learning and fine-tuning, qualities that allow them to evolve over time.

LVMs can also scale well, handling large and diverse data sets and hardware. They can use powerful GPUs, TPUs, or clusters, optimizing parallel computations for faster and more accurate results.

They can also be compressed and optimized for edge devices using pruning, quantization, or distillation techniques. Pruning removes unnecessary or redundant parameters, quantization reduces the number of bits used to represent each parameter, and distillation transfers the knowledge from a larger model to a smaller one.

These techniques help LVMs reduce their size, memory, and latency while preserving their performance, making them adaptable and scalable across applications and hardware.

LVMs are still evolving and improving. One key aspect is the development of domain-specific models, like LandingAI’s LandingLens, that are fine-tuned to help build models with small datasets for specific tasks.

Another path involves multimodal capabilities, demonstrated by OpenAI’s DALL-E, which integrates diverse data types for more immersive interactions.

Additionally, the ascent of generative AI LVMs, such as OpenAI’s Jukebox, suggests a future where these models create novel content from user input, offering personalized and creative experiences.

Use Cases of LVMs

LVMs are already widely utilized across various domains, demonstrating their versatility and impact.

In healthcare, these models aid in disease diagnosis and personalized treatments. For instance, Google’s AlphaFold, an LVM, predicts the 3D structure of proteins from their amino acid sequences, which are the building blocks of proteins. This is essential for understanding the function and interactions of proteins involved in various diseases like COVID-19, Alzheimer’s, or cancer.

The educational sector also benefits from LVMs like Duolingo’s BirdBrain, a machine learning model that personalizes the language learning experience for each user. BirdBrain predicts the difficulty level and the optimal timing of the exercises based on the user’s knowledge and progress. BirdBrain is an example of an LVM that uses a vision transformer architecture and has over 300 million parameters.

In commerce, LVMs can also create and recommend fashion items based on visual and textual inputs. For example, Alibaba’s FashionAI system uses LVMs to analyze product images and customer preferences and provide personalized mix-and-match suggestions on an intelligent mirror inside a concept store. The system also integrates augmented reality for virtual try-on and styling services, providing a more convenient and satisfying shopping experience.

The Challenges of LVMs

Despite their numerous benefits, LVMs have challenges. High cost is a significant drawback, as training and running LVMs demand substantial data and computational resources, leading to financial and environmental concerns.

Another challenge is the potential for high bias, as LVMs can inherit and amplify biases in their training data, which can result in unfair outcomes and discrimination.

In addition, LVMs need more transparency and explainability, complicating efforts to understand and trust their decision-making processes.

Another risk associated with LVMs is multifaceted, encompassing ethical, legal, privacy, and security concerns. This risk is exemplified by misuse, such as creating deepfake videos for nonconsensual purposes or potential involvement in cyberattacks like phishing and ransomware.

The Bottom Line

LVMs are reforming computer vision tasks. Their ability to process visual data, adapt to diverse domains, and generate synthetic content is still in the early days — but moving fast.

Despite challenges like high costs and ethical concerns, LVMs offer immense benefits, from healthcare advancements to personalized learning and enhanced entertainment.