Abstract: In an example embodiment, a solution is provided where a multi-task two-tower machine learning model is utilized to evaluate content against multiple different guidelines. Each of these guidelines represent a different task in the single multi-task two-tower machine learning model, and knowledge can be shared between the layers of the model devoted to each guideline, eliminating the need for separately trained models and also greatly improving the reliability of the predictions. The multi-task two-tower machine learning model is also capable of evaluating both text content and image content, via a respective tower utilized to embed such content into a shared latent n-dimensional space.

Abstract: In an example embodiment, a solution is provided where a multi-task two-tower machine learning model is utilized to evaluate content against multiple different guidelines. Each of these guidelines represent a different task in the single multi-task two-tower machine learning model, and knowledge can be shared between the layers of the model devoted to each guideline, eliminating the need for separately trained models and also greatly improving the reliability of the predictions. The multi-task two-tower machine learning model is also capable of evaluating both text content and image content, via a respective tower utilized to embed such content into a shared latent n-dimensional space.

Abstract: In an example embodiment, a solution is provided where a multi-task two-tower machine learning model is utilized to evaluate content against multiple different guidelines. Each of these guidelines represent a different task in the single multi-task two-tower machine learning model, and knowledge can be shared between the layers of the model devoted to each guideline, eliminating the need for separately trained models and also greatly improving the reliability of the predictions. The multi-task two-tower machine learning model is also capable of evaluating both text content and image content, via a respective tower utilized to embed such content into a shared latent n-dimensional space.

Abstract: An image of an object from a known object class is synthesized by first obtaining reflectance fields for various training objects from the object class. A reflectance field model is defined for the object class using a combination of the reflectance fields of the training objects. The parameters of the reflectance field model are optimized to estimate a particular reflectance field of a particular object from the object class given one or more input images of the particular object. The particular reflectance field is fitted to the particular object, and then the new image of the particular object is synthesized by changing the illumination parameters of the particular fitted reflectance field model after the fitting.

Abstract: An image of an object from a known object class is synthesized by first obtaining reflectance fields for various training objects from the object class. A reflectance field model is defined for the object class using a combination of the reflectance fields of the training objects. The parameters of the reflectance field model are optimized to estimate a particular reflectance field of a particular object from the object class given one or more input images of the particular object. The particular reflectance field is fitted to the particular object, and then the new image of the particular object is synthesized by changing the illumination parameters of the particular fitted reflectance field model after the fitting.