Abstract: A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.

Abstract: A neural network architecture for attention-based efficient model adaptation is disclosed. A method includes accessing an input vector, the input vector comprising a numeric representation of an input to a neural network. The method includes providing the input vector to the neural network comprising a plurality of ordered layers, wherein each layer in at least a subset of the plurality of ordered layers is coupled with an adaptation module, wherein the adaptation module receives a same input value as a coupled layer for the adaptation module, and wherein an output value of the adaptation module is pointwise multiplied with an output value of the coupled layer to generate a next layer input value. The method includes generating an output of the neural network based on an output of a last one of the plurality of ordered layers in the neural network.

Abstract: Examples are disclosed that relate to computing devices and methods for synthesizing a novel pose of an object. One example provides a method comprising receiving a reference image of an object corresponding to an original viewpoint. The reference image of the object is translated into a depth map of the object, and a new depth map of the object is synthesized to correspond to a new viewpoint. A new image of the object is generated from the new viewpoint based on the new depth map of the object and the reference image of the object.

Abstract: Examples are disclosed that relate to computing devices and methods for synthesizing a novel pose of an object. One example provides a method comprising receiving a reference image of an object corresponding to an original viewpoint. The reference image of the object is translated into a depth map of the object, and a new depth map of the object is synthesized to correspond to a new viewpoint. A new image of the object is generated from the new viewpoint based on the new depth map of the object and the reference image of the object.

Abstract: A neural network architecture for attention-based efficient model adaptation is disclosed. A method includes accessing an input vector, the input vector comprising a numeric representation of an input to a neural network. The method includes providing the input vector to the neural network comprising a plurality of ordered layers, wherein each layer in at least a subset of the plurality of ordered layers is coupled with an adaptation module, wherein the adaptation module receives a same input value as a coupled layer for the adaptation module, and wherein an output value of the adaptation module is pointwise multiplied with an output value of the coupled layer to generate a next layer input value. The method includes generating an output of the neural network based on an output of a last one of the plurality of ordered layers in the neural network.