Meta's New Segment Anything Model for Identification Is a Big Deal, Experts Say

 Computers are getting closer to human levels of visual perception with improved abilities to detect and recognize objects. 


Meta is rolling out an AI image segmentation model that can see and isolate objects in an image even if it never saw them before. The model, called Segment Anything (SAM), brings faster and more accurate image recognition and reduces reliance on humans to label objects. 

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"The flexibility of SAM allows it to be applied to a variety of industries and use cases, such as agriculture, retail, medical imagery, and geospatial imagery, leading to improved outcomes and increased efficiency," Ulrik Stig Hansen, the president at Encord, a software company that recently integrated SAM into its product, told Lifewire, in an email interview. 

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Image Segmentation in AI

Meta's software could be a significant boon to computer vision researchers. SAM is an image segmentation model that can respond to text prompts or user clicks to isolate specific objects within an image, Meta researchers wrote in a blog post. 

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One fundamental problem in the field of computer vision is how to get the software to recognize and understand objects it hasn't seen before. The approach used by SAM is image segmentation, which involves dividing an image into multiple segments or regions, each representing a specific object or area of interest. 


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SAM uses interactive segmentation, with a human guiding the model by refining results, and automatic segmentation, where the model does it by itself after being trained on hundreds or thousands of annotated objects. The dataset used to teach SAM contains more than 1.1 billion segmentation masks collected from 11 million licensed and privacy-preserving images, meaning it has 400 times more masks than any existing dataset.

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The vast dataset lets SAM generalize new types of objects and images beyond what it was trained on. As a result, the researchers claim that AI practitioners will no longer need to collect their own segmentation data and instead can use the open-source SAM model. 

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SAM has a head start in recognizing objects and has already learned a general idea of what things are. It can generate "masks" for any object in any image or video, even for objects and images it has not previously encountered. Masking involves identifying an object based on the changes in contrast at its edges and separating it from the rest of the scene. Meta researchers said SAM is general enough for many uses. 


"In the future, SAM could be used to help power applications in numerous domains that require finding and segmenting any object in any image," the researchers said. "For the AI research community and others, SAM could become a component in larger AI systems for a more general multimodal understanding of the world, for example, understanding a webpage's visual and text content. In the AR/VR domain, SAM could enable selecting an object based on a user's gaze and then 'lifting' it into 3D."

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