Adobe, Inc.

Vinh Ngoc Khuc

Deepak Pai

Shankar Venkitachalam

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Kumar Mrityunjay Singh

Kunal Kumar Jain

Hyman Chung

Yaman Kumar

Simra Shahid

Sukriti Verma

Vijay Srivastava

Nimish Srivastav

Shirsh Bansal

Sandipan Karmakar

Sean Steimer

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Ajinkya Gorakhnath Kale

Debraj Basu

Systems and methods for image processing are described. Embodiments of the present disclosure obtain a text prompt for text guided image generation. A multi-modal encoder of an image processing apparatus encodes the text prompt to obtain a text embedding. A diffusion prior model of the image processing apparatus converts the text embedding to an image embedding. A latent diffusion model of the image processing apparatus generates an image based on the image embedding, wherein the image includes an element described by the text prompt.

Prior guided latent diffusion

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

Generating weighted contextual themes to guide unsupervised keyphrase relevance models

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Campbell, CA, United States of America

Vinh Ngoc Khuc