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公开(公告)号:US20230325597A1
公开(公告)日:2023-10-12
申请号:US17658855
申请日:2022-04-12
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Rishav Agarwal , Rishabh Purwar , Ajinkya Gorakhnath Kale , Sanyam Jain
IPC: G06F40/284 , G06F40/253 , G06F40/35
CPC classification number: G06F40/284 , G06F40/253 , G06F40/35
Abstract: Systems and methods for natural language processing are described. Embodiments of the present disclosure receive plain text comprising a sequence of text entities; generate a sequence of entity embeddings based on the plain text, wherein each entity embedding in the sequence of entity embeddings is generated based on a text entity in the sequence of text entities; generate style information for the text entity based on the sequence of entity embeddings; and generate a document based on the style information.
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公开(公告)号:US12260480B2
公开(公告)日:2025-03-25
申请号:US18178791
申请日:2023-03-06
Applicant: Adobe Inc.
Inventor: Sukriti Verma , Venkata naveen kumar Yadav Marri , Ritiz Tambi , Pranav Vineet Aggarwal , Peter O'Donovan , Midhun Harikumar , Ajinkya Kale
IPC: G06T11/60 , G06F3/0482
Abstract: Embodiments are disclosed for machine learning-based generation of recommended layouts. The method includes receiving a set of design elements for performing generative layout recommendation. A number of each type of design element from the set of design elements is determined. A set of recommended layouts are generated using a trained generative layout model and the number and type of design elements. The set of recommended layouts are output.
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公开(公告)号:US20250117970A1
公开(公告)日:2025-04-10
申请号:US18637654
申请日:2024-04-17
Applicant: ADOBE INC.
Inventor: Sachin Madhav Kelkar , Hareesh Ravi , Ritiz Tambi , Ajinkya Gorakhnath Kale
IPC: G06T11/00
Abstract: A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a text prompt and a conditioning attribute. The text prompt is encoded to obtain a text embedding. The conditioning attribute is encoded to obtain an attribute embedding. Then a synthesized image is generated using an image generation model based on the text embedding and the attribute embedding. The synthesized image has the conditioning attribute and depicts an element of the text prompt.
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公开(公告)号:US20250117967A1
公开(公告)日:2025-04-10
申请号:US18443590
申请日:2024-02-16
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Purvak Lapsiya , Ajinkya Gorakhnath Kale , Pranav Vineet Aggarwal , Nikolaos Vlassis
IPC: G06T11/00 , G06F40/166 , G06V10/764
Abstract: A method, apparatus, non-transitory computer readable media, and system for image generation include obtaining an input text prompt and an indication of a level of a target characteristic, where the target characteristic comprises a characteristic used to train an image generation model. Some embodiments generate an augmented text prompt comprising the input text and an objective text corresponding to the level of the target characteristic. Some embodiments generate, using the image generation model, an image based on the augmented text prompt, where the image depicts content of the input text prompt and has the level of the target characteristic.
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公开(公告)号:US20240095275A1
公开(公告)日:2024-03-21
申请号:US17933595
申请日:2022-09-20
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Ajinkya Gorakhnath Kale
IPC: G06F16/532
CPC classification number: G06F16/532
Abstract: Systems and methods for query processing are described. Embodiments of the present disclosure identify an original query; generate a plurality of expanded queries by generating a plurality of additional phrases based on the original query using a causal language model (CLM) and augmenting the original query with each of the plurality of additional phrases, respectively; and provide a plurality of images in response to the original query, wherein the plurality of images are associated with the plurality of expanded queries, respectively.
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公开(公告)号:US20220138439A1
公开(公告)日:2022-05-05
申请号:US17088847
申请日:2020-11-04
Applicant: Adobe Inc.
Inventor: Ritiz Tambi , Pranav Aggarwal , Ajinkya Kale
IPC: G06F40/58 , G06F40/117
Abstract: Introduced here is an approach to translating tags assigned to digital images. As an example, embeddings may be extracted from a tag to be translated and the digital image with which the tag is associated by a multimodal model. These embeddings can be compared to embeddings extracted from a set of target tags associated with a target language by the multimodal model. Such an approach allows similarity to be established along two dimensions, which ensures the obstacles associated with direct translation can be avoided.
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公开(公告)号:US12153619B2
公开(公告)日:2024-11-26
申请号:US17933595
申请日:2022-09-20
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Ajinkya Gorakhnath Kale
IPC: G06F16/00 , G06F16/532
Abstract: Systems and methods for query processing are described. Embodiments of the present disclosure identify an original query; generate a plurality of expanded queries by generating a plurality of additional phrases based on the original query using a causal language model (CLM) and augmenting the original query with each of the plurality of additional phrases, respectively; and provide a plurality of images in response to the original query, wherein the plurality of images are associated with the plurality of expanded queries, respectively.
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公开(公告)号:US12056453B2
公开(公告)日:2024-08-06
申请号:US17658855
申请日:2022-04-12
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Rishav Agarwal , Rishabh Purwar , Ajinkya Gorakhnath Kale , Sanyam Jain
IPC: G06F40/20 , G06F40/253 , G06F40/284 , G06F40/35
CPC classification number: G06F40/284 , G06F40/253 , G06F40/35
Abstract: Systems and methods for natural language processing are described. Embodiments of the present disclosure receive plain text comprising a sequence of text entities; generate a sequence of entity embeddings based on the plain text, wherein each entity embedding in the sequence of entity embeddings is generated based on a text entity in the sequence of text entities; generate style information for the text entity based on the sequence of entity embeddings; and generate a document based on the style information.
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公开(公告)号:US20240104131A1
公开(公告)日:2024-03-28
申请号:US17934690
申请日:2022-09-23
Applicant: ADOBE INC.
Inventor: Ritiz Tambi , Ajinkya Gorakhnath Kale
IPC: G06F16/532 , G06F40/284 , G06F40/289
CPC classification number: G06F16/532 , G06F40/284 , G06F40/289
Abstract: Systems and methods for query processing are described. Embodiments of the present disclosure identify a target phrase in an original query, wherein the target phrase comprises a phrase to be replaced in the original query; replace the target phrase with a mask token to obtain a modified query; generate an alternative query based on the modified query using a masked language model (MLM), wherein the alternative query includes an alternative phrase in place of the target phrase that is consistent with a context of the target phrase; and retrieve a search result based on the alternative query.
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公开(公告)号:US11816162B2
公开(公告)日:2023-11-14
申请号:US16944203
申请日:2020-07-31
Applicant: Adobe Inc.
Inventor: Ritiz Tambi , Ajinkya Kale , Tracy Holloway King
IPC: G06F16/90 , G06F16/9032 , G06F40/263 , G06F40/242 , G06F16/903 , G06N20/00 , G06F18/214 , G06F18/2413
CPC classification number: G06F16/90324 , G06F16/90344 , G06F18/2155 , G06F18/24147 , G06F40/242 , G06F40/263 , G06N20/00
Abstract: Systems and methods are disclosed for search query language identification. One method comprises generating a seed dictionary comprising a plurality of labeled dictionary terms and receiving a plurality of unlabeled sample query terms. The plurality of unlabeled sample query terms are compared to the plurality of labeled dictionary terms at a first time, and a first set of labeled sample query terms are generated by labeling at least a subset of the plurality of unlabeled sample query terms based on the first comparison. Remaining unlabeled sample query terms are then compared with the first set of labeled sample query terms at a second time, and a second set of labeled sample query terms are generated by labeling the remaining unlabeled sample query terms based on the second comparison. The first and second sets of labeled sample query terms are provided to a machine learning model configured for query language prediction.
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