Adobe, Inc.

Fengbin Chen

Venkat Barakam

Benjamin Leviant

Zhe Lin

Ajinkya Gorakhnath Kale

Baldo Antonio Faieta

Trung Huu Bui

Kate Sousa

Francois Guerin

Jayant Kumar

Kerem Can Turgutlu

Gaurav Kukal

Vipul Dalal

Amine Ben Khalifa

Sumeet Zaverilal Gala

Systems and methods for information retrieval are described. Embodiments generate a dense embedding for each of a plurality of media objects to be searched, generate a sparse embedding for each of the media objects using an encoder that takes the dense embedding as an input, wherein the sparse embedding satisfies a sparsity constraint that is applied to at least one layer of the encoder during training, and perform a search on the plurality of media objects based at least in part on the sparse embedding.

Retrieval aware embedding

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

Generating contextual tags for digital content

Growing community of inventors

San Jose, CA, United States of America

Fengbin Chen