Adobe, Inc.

Tripti Shukla

Abhilasha Sancheti

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Sumit Shekhar

Aanisha Bhattacharyya

Abhinav Bohra

Ayudh Saxena

Bhanu Prakash Reddy Guda

Jeevana Kruthi Karnuthala

Vishwa Vinay

Paridhi Maheshwari

Srikrishna Karanam

Manoj Ghuhan Arivazhagan

Akhash Nakkonda Amarnath

Simarpreet Singh Saluja

Divya Kothandaraman

Divyam Gupta

Sarthak Rout

Nakula Neeraje

Praneetha Vaddamanu

Khyathi Vagolu

Vedant Raval

Systems and methods for automatically generating graphic design documents are described. Embodiments include identifying an input text that includes a plurality of phrases; obtaining one or more images based on the input text; encoding an image of the one or more images in a vector space using a multimodal encoder to obtain a vector image representation; encoding a phrase from the plurality of phrases in the vector space using the multimodal encoder to obtain a vector text representation; selecting an image text combination including the image and the phrase by comparing the vector image representation and the vector text representation; selecting a design template from a plurality of candidate design templates based on the image text combination; and generating a document based on the design template, wherein the document includes the at least one image and the at least one phrase.

Automatically generating graphic design variants from input text

Systems and methods for machine learning are described. The systems and methods include receiving target training data including a training image and ground truth label data for the training image, generating source network features for the training image using a source network trained on source training data, generating target network features for the training image using a target network, generating at least one attention map for training the target network based on the source network features and the target network features using a guided attention transfer network, and updating parameters of the target network based on the attention map and the ground truth label data.

System and methods for active domain adaptation

Techniques are described that support automated generation of a digital document from digital videos using machine learning. The digital document includes textual components that describe a sequence of entity and action descriptions from the digital video. These techniques are usable to generate a single digital document based on a plurality of digital videos as well as incorporate user-specified constraints in the generation of the digital document.

Automated digital document generation from digital videos

An illustrator system determines, for each feature of a set of features, a feature representation for an electronic document displayed via a user interface, based on a plurality of elements of the electronic document. The system receives a selection from among the set of features of (1) a query feature and of (2) a target feature and determines, for each replacement template of a set of replacement templates, a compatibility score based on the feature representation for the electronic document determined for the query feature and a target feature representation of the replacement template determined for the target feature, the representations being determined in a joint representation space. The system selects one or more replacement electronic documents based on the determined compatibility scores. The system displays a preview for each replacement electronic document and displays a particular replacement electronic document responsive to receiving a selection of a preview.

Cross view template recommendation system

Certain embodiments involve a template-based redesign of documents based on the contents of documents. For instance, a computing system selects a template for modifying an input document. To do so, the computing system uses a generative adversarial network to generate an interpolated layout image from an input layout image, which represents the input document, and a template layout image, which represents the selected template. The computing system matches the input element to an interpolated element from the interpolated layout image. The computing system generates an output document by, for example, modifying a layout of the input document to match the interpolated layout image, such as by fitting the input element into a shape of the interpolated element.

Template-based redesign of a document based on document content

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Uttar Pradesh, India

Tripti Shukla