Adobe, Inc.

Other

Walter Wei-Tuh Chang

Scott David Cohen

Hailin Jin

Jonathan Worthen Brandt

Nathan Aaron Carr

Gavin Stuart Peter Miller

Zeke Koch

Sachin Soni

Radomir Mech

Anmol Dhawan

Ashish Duggal

Trung Huu Bui

Frederic Thevenet

Carl Dockhorn

Laurie Marie Byrum

Sean Fitzgerald

Frieder Ludwig Anton Ganz

Ragunandan Rao Malangully

Brian Lynn Price

Franck Dernoncourt

Shawn Alan Gaither

Michael F Kraley

Hung Hai Bui

Kenneth Edward Feuerman

Ankit Bal

Pranjal Daga

Jason Guthrie Waters

Mohamed Hamdy Mahmoud Abdelbaky Elhoseiny

Shantanu Kumar

Zhe Lin

Viswanathan Swaminathan

Eunyee Koh

Byungmoon Kim

Russell Robert Stringham

Seokhwan Kim

Geoffrey King Baum

Antoine Amanieux

Nham Van Le

Laurel Alisa Warrell

Codruta L Girlea

Conversational image editing and enhancement techniques are described. For example, an indication of a digital image is received from a user. Aesthetic attribute scores for multiple aesthetic attributes of the image are generated. A computing device then conducts a natural language conversation with the user to edit the digital image. The computing device receives inputs from the user to refine the digital image as the natural language conversation progresses. The computing device generates natural language suggestions to edit the digital image based on the aesthetic attribute scores as part of the natural language conversation. The computing device provides feedback to the user that includes edits to the digital image based on the series of inputs. The computing device also includes as feedback natural language outputs indicating options for additional edits to the digital image based on the series of inputs and the previous edits to the digital image.

Digital media environment for conversational image editing and enhancement

The present invention is directed towards providing automated workflows for the identification of a reading order from text segments extracted from a document. Ordering the text segments is based on trained natural language models. In some embodiments, the workflows are enabled to perform a method for identifying a sequence associated with a portable document. The methods includes iteratively generating a probabilistic language model, receiving the portable document, and selectively extracting features (such as but not limited to text segments) from the document. The method may generate pairs of features (or feature pair from the extracted features). The method may further generate a score for each of the pairs based on the probabilistic language model and determine an order to features based on the scores. The method may provide the extracted features in the determined order.

Probabilistic language models for identifying sequential reading order of discontinuous text segments

Techniques and systems are described to model and extract knowledge from images. A digital medium environment is configured to learn and use a model to compute a descriptive summarization of an input image automatically and without user intervention. Training data is obtained to train a model using machine learning in order to generate a structured image representation that serves as the descriptive summarization of an input image. The images and associated text are processed to extract structured semantic knowledge from the text, which is then associated with the images. The structured semantic knowledge is processed along with corresponding images to train a model using machine learning such that the model describes a relationship between text features within the structured semantic knowledge. Once the model is learned, the model is usable to process input images to generate a structured image representation of the image.

Structured knowledge modeling and extraction from images

Various methods and systems for performing analytics based on hierarchical categorization of content are provided. Analytics can be performed using an index building workflow and a classification workflow. In the index building workflow, documents are received and analyzed to extract features from the documents. Hierarchical category paths can be identified for the features. The documents are indexed to support searching the documents for the hierarchical category paths. In the classification workflow, a query, that includes or references content, may be received and analyzed to extract features from the content. The features are executed against a search engine that returns search result documents associated with hierarchical category paths. The hierarchical category paths from the search result documents may be used to generate a topic model of the content associated with the query. The topic model, used for web analytics, includes scores for the hierarchical category paths and for enumerated category topics.

Analytics based on scalable hierarchical categorization of web content

A method for recommending hashtags includes determining keywords from a post planned for publishing by a publisher. An input criteria comprising at least one of age group, geographical location, date range, or a keyword is received. Previous posts associated with the keywords and satisfying the input criteria are obtained. The previous posts are categorized into one or more categories based on sentiment of each post and for each category hashtags used in the obtained previous posts in that category are determined. The hashtags are ranked based on predefined criteria comprising at least one of frequency of appearance of respective hashtag in posts, number of likes or shares or retweets of post comprising respective hashtag, number of followers of person who used respective hashtag, or sentiment of post comprising respective hashtag. The hashtags are then recommended, based on ranking, to the publisher for use with the post planned for publishing.

Ranking and recommending hashtags

Techniques are disclosed for generating a structured transcription from a speech file. In an example embodiment, a structured transcription system receives a speech file comprising speech from one or more people and generates a navigable structured transcription object. The navigable structured transcription object may comprise one or more data structures representing multimedia content with which a user may navigate and interact via a user interface. Text and/or speech relating to the speech file can be selectively presented to the user (e.g., the text can be presented via a display, and the speech can be aurally presented via a speaker).

Emphasizing key points in a speech file and structuring an associated transcription

Image search techniques and systems involving emotions are described. In one or more implementations, a digital medium environment of a content sharing service is described for image search result configuration and control based on a search request that indicates an emotion. The search request is received that includes one or more keywords and specifies an emotion. Images are located that are available for licensing by matching one or more tags associated with the image with the one or more keywords and as corresponding to the emotion. The emotion of the images is identified using one or more models that are trained using machine learning based at least in part on training images having tagged emotions. Output is controlled of a search result having one or more representations of the images that are selectable to license respective images from the content sharing service.

Image search using emotions

In implementations of collecting multimodal image editing requests (IERs), a user interface is generated that exposes an image pair including a first image and a second image including at least one edit to the first image. A user simultaneously speaks a voice command and performs a user gesture that describe an edit of the first image used to generate the second image. The user gesture and the voice command are simultaneously recorded and synchronized with timestamps. The voice command is played back, and the user transcribes their voice command based on the play back, creating an exact transcription of their voice command. Audio samples of the voice command with respective timestamps, coordinates of the user gesture with respective timestamps, and a transcription are packaged as a structured data object for use as training data to train a neural network to recognize multimodal IERs in an image editing application.

Collecting multimodal image editing requests

Automated workflows for identification of reading order from text segments using probabilistic language models

Highlighting key portions of text within a document is described. A document having text is obtained, and key portions of the document are determined using summarization techniques. Key portion data indicative of the key portions is generated and maintained for output to generate a highlighted document in which highlight overlays are displayed over or proximate the determined key portions of the text within the document. In one or more implementations, reader interactions with the highlighted document are monitored to generate reader feedback data. The reader feedback data may then be combined with the output of the summarization techniques in order to adjust the determined key portions. In some cases, the reader feedback data may also be used to improve the summarization techniques.

Highlighting key portions of text within a document

Structured knowledge modeling, extraction and localization from images

Content creation and sharing integration techniques and systems are described. In one or more implementations, techniques are described in which modifiable versions of content (e.g., images) are created and shared via a content sharing service such that image creation functionality used to create the images is preserved to permit continued creation using this functionality. In one or more additional implementations, image creation functionality employed by a creative professional to create content is leveraged to locate similar images from a content sharing service.

Integration of content creation and sharing

Growing community of inventors

San Jose, CA, United States of America

Walter Wei-Tuh Chang