LVMs enable enterprises to solve computer vision problems more quickly; Landing AI to help enterprises build and run LVMs at the edge, on-prem, and in the cloud
Landing AI, the leading computer vision cloud platform, announced the launch of its capability to help enterprises build domain-specific large vision models (LVMs). The groundbreaking advancement enables businesses with vast image libraries to bring artificial intelligence to their proprietary image data, allowing versatile applications within their domain to meet business needs.
Like large language models (LLMs) enabled the text-prompting revolution, large vision models will enable the vision revolution. Enterprises will unlock intelligence from their images at a much faster pace than before while protecting their privacy with domain specific LVMs. Many companies have hundreds of thousands, millions or billions of images, most of which differ from internet images that other models have been trained on.
With Landing AI’s innovative LVM solution, companies can take unlabeled image data and create high-performing LVMs that serve as a foundation to solve a diverse set of computer vision tasks in their specific domains. This will occur much faster than with traditional approaches because companies will save months of work by not having to label vast image libraries. And they’ll see improved accuracy and performance in terms of completed computer vision tasks given the intelligence of the LVM.
Landing AI’s domain-specific LVMs are trained using the enterprise’s private images. Then, the companies can accelerate the implementation of custom vision solutions with Landing AI’s end-to-end Computer Vision platform.
“Enterprises’ Large Vision Model revolution is following the Large Language Model revolution, but with one key difference: Whereas internet text that LLMs learned from are similar enough to most corporate text for the model to apply, a lot of companies in manufacturing, life sciences, geo-spatial data, agriculture, retail, and other sectors have proprietary images that look nothing like the typical Instagram pictures found online,” said Andrew Ng, Landing AI CEO. “That’s why developing domain-specific LVMs is key to unlocking the value of the images in these domains.”
Landing AI is building and running domain specific LVMs for large enterprises who’ve long sought to create value from proprietary images. For example, this will include such things as production line images for finding defects in manufacturing or histopathology images for finding cancer cells in life sciences.
While generic LVMs built on internet images are one size fits all, the Landing AI LVMs focus on one domain at a time, helping solve proprietary problems facing enterprises. Today enterprises spend a non-trivial amount of effort training individual models for each vision task, even when these tasks belong to the same business domain. With domain-specific LVMs, the goal is for companies to use a limited set of LVMs, one for each business domain, and meet their needs to solve a multitude of vision tasks in each domain.
With LVMs, companies will more quickly pinpoint solutions for tasks such as object detection, image segmentation, visual prompting, or other AI vision enabled applications.
The addition of LVM capability to Landing AI underscores Landing AI’s continued investment in Generative AI. In April, it announced its revolutionary Visual Prompting capability as part of its LandingLens offering. This technology takes the framework of text prompting in technologies such as ChatGPT and brings it to computer vision.
To learn more about how you can deploy LVMs using Landing AI’s computer vision solutions, please visit www.landing.ai/lvm
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