Adobe, Inc.

Xiaoyang Li

John Philip Collomosse

Geoffrey Oxholm

Maksym Andriushchenko

Abhishek Lalwani

Systems and methods for image processing are described. Embodiments of the present disclosure identify an original image depicting a face, identify a scribble image including a mask that indicates a portion of the original image for adding makeup to the face, and generate a target image depicting the face using a machine learning model based on the original image and the scribble image, where the target image includes the makeup in the portion indicated by the scribble image.

Controllable makeup transfer via scribble inputs

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.

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San Francisco, CA, United States of America

Xiaoyang Li