RESTRICTED MULTI-SCALE INFERENCE FOR MACHINE LEARNING

Techniques for utilizing multiple scales of images as input to machine learning (ML) models are discussed herein. Operations can include providing an image associated with a first scale to a first ML model. An output of the first ML model can include a first bounding box indicative of a first region...

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Bibliographic Details
Main Authors Tariq, Sarah, Philbin, James William Vaisey, Goel, Kratarth
Format Patent
LanguageEnglish
Published 24.08.2023
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Summary:Techniques for utilizing multiple scales of images as input to machine learning (ML) models are discussed herein. Operations can include providing an image associated with a first scale to a first ML model. An output of the first ML model can include a first bounding box indicative of a first region of the image representing a first object, with the first bounding box falling within a first range of sizes. Next, a scaled image can be generated by scaling the image. The scaled image can be provided to a second ML model, which can output a second bounding box indicative of a second region of the image representing a second object, the second bounding falling within a second range of sizes. Thus, inputting a scaled image to a same ML model (or to different ML models) can result in different detected features in the images.
Bibliography:Application Number: US202318114531