Adobe, Inc.

Kate Sousa

Zhe Lin

Baldo Antonio Faieta

Ajinkya Gorakhnath Kale

Trung Huu Bui

Benjamin Leviant

Fengbin Chen

Venkat Barakam

Francois Guerin

Saeid Motiian

Alex Charles Filipkowski

Pramod Srinivasan

Systems, methods, and non-transitory computer-readable media are disclosed for determining multi-term contextual tags for digital content and propagating the multi-term contextual tags to additional digital content. For instance, the disclosed systems can utilize search query supervision to determine and associate multi-term contextual tags (e.g., tags that represent a specific concept based on the order of the terms in the tag) with digital content. Furthermore, the disclosed systems can propagate the multi-term contextual tags determined for the digital content to additional digital content based on similarities between the digital content and additional digital content (e.g., utilizing clustering techniques). Additionally, the disclosed systems can provide digital content as search results based on the associated multi-term contextual tags.

Propagating multi-term contextual tags to digital content

Based on a received digital image and text, a neural network trained to identify candidate text placement areas within images may be used to generate a mask for the digital image that includes a candidate text placement area. A bounding box for the digital image may be defined for the text and based on the candidate text placement area, and the text may be superimposed onto the digital image within the bounding box.

Text placement within images using neural networks

Generating contextual tags for digital content

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San Francisco, CA, United States of America

Kate Sousa