Domain-Specific Large Vision Models: Revolutionizing the Way We See the World!

We are excited to announce the latest innovation from Landing AI, domain specific Large Vision Models (LVMs), continuing upon our successful launch of Visual Prompting earlier this year.?

Similar to what we have seen with large language models (LLMs) enabling the text revolution, we believe large vision models are now enabling the vision revolution, but with one key difference: Whereas LLMs trained on internet text work well on most companies’ proprietary documents, most businesses’ images look nothing like Instagram and other internet images, which is why an LVM adapted to be specific to the company’s use case is needed.? We have seen domain specific LVMs demonstrate superior performance with a deeper and more nuanced understanding of visual content, in tasks such as image classification, object detection and segmentation with its ability to capture intricate patterns and features in data.

Over the past few years, the field of artificial intelligence and computer vision specifically has witnessed a transformative shift with the advent of vision transformers and general large vision models.? These models, powered by deep learning architectures, have demonstrated remarkable capabilities in tasks related to computer vision. At the forefront of this revolution for consumers are companies like OpenAI with models like GPT-4V and Meta with SAM (Segment Anything Model) as well as Landing AI focusing more squarely on enterprises with our introduction of domain specific LVMs. A domain specific LVM, which has been trained on a large set of images, enables an enterprise to quickly adapt it to a myriad of use cases within that domain.?

For businesses that have a large, proprietary set of image or video data within a specialized domain, this offers a recipe to unlock the tremendous value latent in that data.

Generic LVMs (initially trained on internet images) are already having an impact across multiple industries. However, we expect domain specific LVMs – tuned to a particular sector or domain – to be a significant accelerator.?

In manufacturing, LVMs already contribute to quality control by inspecting and identifying defects in products with a higher level of accuracy than traditional computer vision solutions.? In the automotive industry, these models will be critical to solving the challenges still being faced around self-driving cars, by enhancing their ability to perceive and interpret the surrounding environment in ways previously unobtainable.?

In healthcare, we have seen LVM models being utilized for medical image analysis, aiding in the diagnosis of diseases and identifying anomalies in medical scans.

Financial institutions could benefit from using LVMs for fraud detection and risk assessment by analyzing vast datasets to identify irregular patterns and potential risks. In marketing and e-commerce, these models help aid in personalized recommendations, improving user experience and engagement.

But adapting LVMs to each application in these domains is still time-consuming. By building domain-specific LVMs adapted to such domains, it will become dramatically more efficient to have computers understand, analyze and process images from these domains.

Conclusion

We believe domain specific LVMs will become indispensable in a wide range of real-world scenarios, revolutionizing industries and enhancing the efficiency and capabilities of various systems. Their ability to learn complex patterns from massive image datasets will make them an invaluable tool for tackling diverse challenges across different domains.

Landing AI has been working with organizations that have 100K to over 1 billion images. Does your organization have a large (100K images or more) set of images that look different from typical internet images? If you want to see if it’s possible to extract significant value from your data using domain-specific Large Vision Models, submit a request to Start Your LVM Journey .