Abstract: Method, system, and computer-readable media for modifying images are disclosed. Main and reference image are received. Location points of content within main image that is candidate for match with reference image are determined. Main image and location points are processed using image segmentation foundation model to generate image mask. Image mask is received. First, degree of similarity between image mask and reference image is determined. Second, presence of reference image in main image at location points is determined, based on result of first determining. Modified image is generated from main image. Modified image is generated by one of removing reference image from main image when reference image is present in main image at allowable location, relocating location of reference image in main image when reference image is present in main image at unallowable location, or adding reference image to main image when reference image is absent in main image.

Abstract: Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.

Abstract: Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.