Adobe, Inc.

Hyman Chung

Balaji Krishnamurthy

Nikaash Puri

Mihir Naware

Yaman Kumar

Kumar Mrityunjay Singh

Kunal Kumar Jain

Matthew William Rozen

Somesh Singh

Pranjal Prasoon

William Brandon George

Deepak Pai

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Sukriti Verma

Brandon Pack

Simra Shahid

Vinh Ngoc Khuc

Vijay Srivastava

Nimish Srivastav

Horea Bacila

Alexander Klimetschek

Timothy Chia-chi Liu

Mihai Corlan

Abhinav Chauhan

James Patrick O'Reilly

Clinton Hansen Goudie-Nice

Suman Basetty

Umang Moorarka

Florin Silviu Iordache

Sean Steimer

Sandipan Karmakar

Shirsh Bansal

Niyati Himanshu Chhaya

Sharmila Reddy Nangi

Harshit Nyati

Nikitha Srikanth

Seoyoung Park

Nithyakala Sainath

Nisarg Shailesh Joshi

Sopan Khosla

Nikhil Kaushik

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

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

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

A text style transfer system is described that generates different stylized versions of input text by rewriting the input text according to a target style. To do so, the text style transfer system employs a variational autoencoder to derive separate content and style representations for the input text, where the content representation specifies semantic information conveyed by the input text and the style representation specifies one or more style attributes expressed by the input text. The style representation using counterfactual reasoning to identify different transfer strengths for applying the target style to the input text. Each transfer strength represents a minimum change to the input text that achieves a different expression of the target style. The transfer strengths are then used to generate style representation variants, which are each concatenated with the content representation of the input text to generate the plurality of different stylized versions of the input text.

Counterfactual text stylization

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San Ramon, CA, United States of America

Hyman Chung