Meta Introduces AI Model Capable of Object Recognition in Images
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An artificial intelligence model that can recognize particular things in an image has been released, according to Meta. In addition to the model, Meta has also released what they call the largest dataset of its kind to date, which includes comments on images.
In a recent blog post, Meta’s research division claimed that the business has created the Segment Anything Model, an advanced object recognition model (SAM). SAM is built to recognize items in pictures and videos even if it hasn’t seen them before during training. The paradigm enables users to choose items by clicking on them or by typing text commands like “cat.” In a test, SAM responded to the stated challenge by precisely drawing boxes around several cats in a photo.
Internally, Meta has been employing SAM-like technologies to identify pictures, filter out objectionable content, and suggest articles to Facebook and Instagram users. According to the company, the distribution of SAM will increase access to this kind of cutting-edge technology outside of their own internal operations.
The SAM model and dataset are available for download from the company under a non-commercial license. Nevertheless, those who upload their own photographs to the prototype that goes with it must consent to exclusively use the tool for study.
“In the future, SAM may be employed to power applications in a variety of fields that call for the identification and segmentation of any object in any image. SAM could be incorporated into bigger AI systems for a more comprehensive multimodal understanding of the world, such as comprehending both the visual and text content of a webpage, according to the AI research community and others. According to Meta’s blog post, SAM could make it possible to choose an object in the AR/VR space based on the user’s gaze and then “lift” it into three dimensions.
The industry titan in technology has hypothesized that SAM may have a variety of uses for content producers, including the capacity to isolate image regions for collages or video editing. The concept could also be helpful in scientific study by enabling researchers to find and follow certain animals or things inside video footage of natural occurrences on Earth or in space.