Adobe, Inc.

Nandan Jha

Vikas Kumar

Sourabh Gupta

Ajay Bedi

Smriti Mehra

Piyush Singh

Jean-Francois Vanreusel

Nishchey Arya

Srijan Sandilya

Rachit Gupta

Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics.

Digital image ordering using object position and aesthetics

Dynamic thumbnails are described. Dynamic thumbnails provide a convenient and automated approach for providing thumbnails that are contextually relevant to a user. In at least some implementations, an input image is analyzed to generate tags describing objects or points of interest within the image, and to generate rectangles that describe the locations within the image that correspond to the generated tags. Various combinations of generated tags are analyzed to determine the smallest bounding rectangle that contains every rectangle associated with the tags in the respective combination, and a thumbnail is created. A user input is received and compared to the tags associated with the generated thumbnails, and a thumbnail that is most relevant to the user input is selected and output to the user.

Dynamic thumbnails

Sketch completion using machine learning in a digital medium environment is described. Initially, a user sketches a digital image, e.g., using a stylus in a digital sketch application. A model trained using machine learning is leveraged to identify and describe visual characteristics of the user sketch. The visual characteristics describing the user sketch are compared to clusters of data generated by the model and that describe visual characteristics of a set of digital sketch images. Based on the comparison, digital sketch images having visual characteristics similar to the user sketch are identified from similar clusters. The similar images are returned for presentation as selectable suggestions for sketch completion of the sketched object.

Sketch completion using machine learning

Technology related to improving synthesis of foreign regional nouns using personalized and culturally correct phonetic transcription is described. The technology includes systems and methods for generating personalized speech by receiving an input, the input including textual data, identifying a regional noun in the textual data, and determining a user accent classification based on a context of the input. The method may further include determining a personalized phonetic transcription of the regional noun corresponding to the user accent classification and using a phonetic inventory stored in a database and outputting the personalized phonetic transcription.

Method to synthesize personalized phonetic transcription

Various embodiments described herein include one or more of systems, software, and methods to receive and automatically route forum postings to individuals tasked with responding to the postings. Some such embodiments extract keywords from the text of such postings and use the extracted keywords and keyword-responder mappings to identify a responder capable of responding to the posting. Some embodiments include generating or updating keyword-responder mappings by identifying keywords included in posting responses made by each responder.

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Ghaziabad, India

Nandan Jha