公开(公告)号：US20210034657A1

公开(公告)日：2021-02-04

申请号：US16525366

申请日：2019-07-29

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/48 ,  G06K9/62 ,  G06F16/43 ,  G06F16/2457

Abstract: 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.

公开(公告)号：US20220237682A1

公开(公告)日：2022-07-28

申请号：US17159554

申请日：2021-01-27

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Zhankui He ,  Zhaowen Wang ,  Zhe Lin ,  Ajinkya Kale ,  Fengbin Chen

IPC: G06Q30/06 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62

Abstract: Systems and methods for item recommendation are described. Embodiments identify a sequence of items selected by a user, embed each item of the sequence of items to produce item embeddings having a reduced number of dimensions, predict a next item based on the item embeddings using a recommendation network, wherein the recommendation network includes a sequential encoder trained based at least in part on a sampled softmax classifier, and wherein predicting the next item represents a prediction that the user will interact with the next item, and provide a recommendation to the user, wherein the recommendation includes the next item.

公开(公告)号：US11971885B2

公开(公告)日：2024-04-30

申请号：US17172986

申请日：2021-02-10

Applicant: ADOBE INC.

Inventor： Fengbin Chen ,  Venkat Barakam ,  Benjamin Leviant ,  Amine Ben Khalifa ,  Kerem Turgutlu ,  Jayant Kumar ,  Sumeet Zaverilal Gala ,  Gaurav Kukal ,  Vipul Dalal

IPC: G06F16/00 ,  G06F16/245 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F16/245 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20220100791A1

公开(公告)日：2022-03-31

申请号：US17544689

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/48 ,  G06K9/62 ,  G06F16/2457 ,  G06F16/43

Abstract: 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.

公开(公告)号：US11741157B2

公开(公告)日：2023-08-29

申请号：US17544689

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/40 ,  G06F16/58 ,  G06F16/48 ,  G06F16/2457 ,  G06F16/43 ,  G06V20/00 ,  G06F18/23213

CPC classification number: G06F16/5866 ,  G06F16/24578 ,  G06F16/43 ,  G06F16/48 ,  G06F18/23213 ,  G06V20/35 ,  G06V2201/10

Abstract: 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.

公开(公告)号：US20220253435A1

公开(公告)日：2022-08-11

申请号：US17172986

申请日：2021-02-10

Applicant: ADOBE INC.

Inventor： Fengbin Chen ,  Venkat Barakam ,  Benjamin Leviant ,  Amine Ben Khalifa ,  Kerem Turgutlu ,  Jayant Kumar ,  Sumeet Zaverilal Gala ,  Gaurav Kukal ,  Vipul Dalal

IPC: G06F16/245 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20250117973A1

公开(公告)日：2025-04-10

申请号：US18903151

申请日：2024-10-01

Applicant: ADOBE INC.

Inventor： Fengbin Chen ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale ,  Hareesh Ravi ,  Venkata Naveen Kumar Yadav Marri

IPC: G06T11/00

Abstract: A method, apparatus, non-transitory computer readable medium, and system for media processing includes obtaining a text prompt and a style input, where the text prompt describes image content and the style input describes an image style, generating a text embedding based on the text prompt, where the text embedding represents the image content, generating a style embedding based on the style input, where the style embedding represents the image style, and generating a synthetic image based on the text embedding and the style embedding, where the text embedding is provided to the image generation model at a first step and the style embedding is provided to the image generation model at a second step after the first step.

公开(公告)号：US20240420389A1

公开(公告)日：2024-12-19

申请号：US18526855

申请日：2023-12-01

Applicant: ADOBE INC.

Inventor： Vineet Batra ,  Sumit Chaturvedi ,  Abhishek Rai ,  Pranav Vineet Aggarwal ,  Ajinkya Gorakhnath Kale ,  Aman Jeph ,  Ankit Phogat ,  Sumit Dhingra ,  Fengbin Chen ,  Kshitiz Garg ,  Milos Hasan ,  Midhun Harikumar ,  Gaurav Suresh Pathak ,  Souymodip Chakraborty

IPC: G06T11/20 ,  G06V10/764 ,  G06V10/774

Abstract: Systems and methods for generating tile-able patterns from text include obtaining a text prompt and generating, by a generation prior model, a latent vector based on the text prompt, where the generation prior model is trained to output vectors within a distribution of tile-able patterns. An image generation model then generates an output image based on the latent vector. The output image comprises a tile-able pattern including an element from the text prompt.

公开(公告)号：US20240355018A1

公开(公告)日：2024-10-24

申请号：US18303898

申请日：2023-04-20

Applicant: Adobe Inc.

Inventor： Pranav Aggarwal ,  Hareesh Ravi ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale ,  Fengbin Chen ,  Venkata Naveen Kumar Yadav Marri

IPC: G06T11/60 ,  G06T5/00 ,  G06T5/50 ,  G06T7/11 ,  G06T7/50 ,  G06T13/00

CPC classification number: G06T11/60 ,  G06T5/50 ,  G06T5/70 ,  G06T7/11 ,  G06T7/50 ,  G06T13/00 ,  G06T2200/24 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20092 ,  G06T2207/20212

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion neural network for mask aware image and typography editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from a base digital image. Moreover, the disclosed systems generate a mask-segmented image by combining a shape mask with the base digital image. In one or more implementations, the disclosed systems utilize noising steps of a diffusion noising model to generate a mask-segmented image noise map from the mask-segmented image. Furthermore, the disclosed systems utilize a diffusion neural network to create a stylized image corresponding to the shape mask from the base image embedding and the mask-segmented image noise map.

公开(公告)号：US11232147B2

公开(公告)日：2022-01-25

申请号：US16525366

申请日：2019-07-29

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/20 ,  G06F16/48 ,  G06K9/62 ,  G06F16/2457 ,  G06F16/43

Abstract: 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.