公开(公告)号：US11886793B2

公开(公告)日：2024-01-30

申请号：US17466679

申请日：2021-09-03

Applicant: ADOBE INC.

Inventor： Zhaowen Wang ,  Saeid Motiian ,  Baldo Faieta ,  Zegi Gu ,  Peter Evan O'Donovan ,  Alex Filipkowski ,  Jose Ignacio Echevarria Vallespi

IPC: G06F40/109 ,  G06F40/166 ,  G06F40/106 ,  G06F40/103

CPC classification number: G06F40/109 ,  G06F40/103 ,  G06F40/106 ,  G06F40/166

Abstract: Embodiments of the technology described herein, are an intelligent system that aims to expedite a text design process by providing text design predictions interactively. The system works with a typical text design scenario comprising a background image and one or more text strings as input. In the design scenario, the text string is to be placed on top of the background. The textual design agent may include a location recommendation model that recommends a location on the background image to place the text. The textual design agent may also include a font recommendation model, a size recommendation model, and a color recommendation model. The output of these four models may be combined to generate draft designs that are evaluated as a whole (combination of color, font, and size) for the best designs. The top designs may be output to the user.

公开(公告)号：US11669566B2

公开(公告)日：2023-06-06

申请号：US17565816

申请日：2021-12-30

Applicant: Adobe Inc.

Inventor： Saeid Motiian ,  Zhe Lin ,  Samarth Gulati ,  Pramod Srinivasan ,  Jose Ignacio Echevarria Vallespi ,  Baldo Antonio Faieta

IPC: G06F16/00 ,  G06F16/583 ,  G06F17/16 ,  G06F16/55 ,  G06F16/532 ,  G06V10/56 ,  G06V10/75 ,  G06V10/762

CPC classification number: G06F16/5838 ,  G06F16/532 ,  G06F16/55 ,  G06F17/16 ,  G06V10/56 ,  G06V10/758 ,  G06V10/763

Abstract: In implementations of multi-resolution color-based image search, an image search system determines a color vector for a query image based on a color histogram of the query image by concatenating two color histograms having different resolutions. The image search system can compute distance measures between the color vector of the query image and color vectors of candidate images. The image search system can select one or more of the candidate images to return based on the distance measures utilizing the distance measures as indication of color similarity of the candidate images to the query image.

公开(公告)号：US20220164380A1

公开(公告)日：2022-05-26

申请号：US17104745

申请日：2020-11-25

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Shabnam Ghadar ,  Saeid Motiian ,  Ratheesh Kalarot ,  Baldo Faieta ,  Alireza Zaeemzadeh

IPC: G06F16/583 ,  G06F16/532 ,  G06F16/538 ,  G06F16/54 ,  G06F16/56 ,  G06N20/00

Abstract: A query image is received, along with a query to initiate a search process to find other images based on the query image. The query includes a preference value associated with an attribute, the preference value indicative of a level of emphasis to be placed on the attribute during the search. A full query vector, which is within a first dimensional space and representative of the query image, is generated. The full query vector is projected to a reduced dimensional space having a dimensionality lower than the first dimensional space, to generate a query vector. An attribute direction corresponding to the attribute is identified. A plurality of candidate vectors of the reduced dimensional space is searched, based on the attribute direction, the query vector, and the preference value, to identify a target vector of the plurality of candidate vectors. A target image, representative of the target vector, is displayed.

公开(公告)号：US20200380027A1

公开(公告)日：2020-12-03

申请号：US16426369

申请日：2019-05-30

Applicant: Adobe Inc.

Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

IPC: G06F16/583 ,  G06N3/08 ,  G06N20/00 ,  G06F16/33 ,  G06F16/538 ,  G06F16/532

Abstract: Multi-modal differential search with real-time focus adaptation techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.

公开(公告)号：US11941727B2

公开(公告)日：2024-03-26

申请号：US17813987

申请日：2022-07-21

Applicant: ADOBE INC.

Inventor： Saeid Motiian ,  Wei-An Lin ,  Shabnam Ghadar

IPC: G06T11/00 ,  G06V40/16

CPC classification number: G06T11/00 ,  G06V40/168 ,  G06T2200/24

Abstract: Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.

公开(公告)号：US11914641B2

公开(公告)日：2024-02-27

申请号：US17186625

申请日：2021-02-26

Applicant: ADOBE INC.

Inventor： Pranav Aggarwal ,  Ajinkya Kale ,  Baldo Faieta ,  Saeid Motiian ,  Venkata naveen kumar yadav Marri

IPC: G06F16/583 ,  G06F40/279 ,  G06N3/08 ,  G06F16/51 ,  G06F16/538 ,  G06F16/532 ,  G06V10/56

CPC classification number: G06F16/5838 ,  G06F16/51 ,  G06F16/532 ,  G06F16/538 ,  G06F40/279 ,  G06N3/08 ,  G06V10/56

Abstract: The present disclosure describes systems and methods for information retrieval. Embodiments of the disclosure provide a color embedding network trained using machine learning techniques to generate embedded color representations for color terms included in a text search query. For example, techniques described herein are used to represent color text in a same space as color embeddings (e.g., an embedding space created by determining a histogram of LAB based colors in a three-dimensional (3D) space). Further, techniques are described for indexing color palettes for all the searchable images in the search space. Accordingly, color terms in a text query are directly converted into a color palette and an image search system can return one or more search images with corresponding color palettes that are relevant to (e.g., within a threshold distance from) the color palette of the text query.

公开(公告)号：US11907280B2

公开(公告)日：2024-02-20

申请号：US17090150

申请日：2020-11-05

Applicant: ADOBE INC.

Inventor： Mikhail Kotov ,  Roland Geisler ,  Saeid Motiian ,  Dylan Nathaniel Warnock ,  Michele Saad ,  Venkata Naveen Kumar Yadav Marri ,  Ajinkya Kale ,  Ryan Rozich ,  Baldo Faieta

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/532 ,  G06F16/2457 ,  G06F16/538 ,  G06F16/583

CPC classification number: G06F16/532 ,  G06F16/24578 ,  G06F16/538 ,  G06F16/5846

Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

公开(公告)号：US20230252071A1

公开(公告)日：2023-08-10

申请号：US18302201

申请日：2023-04-18

Applicant: Adobe Inc.

Inventor： Pramod Srinivasan ,  Zhe Lin ,  Samarth Gulati ,  Saeid Motiian ,  Midhun Harikumar ,  Baldo Antonio Faieta ,  Alex C. Filipkowski

IPC: G06F16/532 ,  G06F16/538 ,  G06F40/30 ,  G06F16/583 ,  G06F16/51 ,  G06F16/54

CPC classification number: G06F16/532 ,  G06F16/51 ,  G06F16/54 ,  G06F16/538 ,  G06F16/583 ,  G06F40/30

Abstract: Keyword localization digital image search techniques are described. These techniques support an ability to indicate “where” a corresponding keyword is to be expressed with respect to a layout in a respective digital image resulting from a search query. The search query may also include an indication of a size of the keyword as expressed in the digital image, a number of instances of the keyword, and so forth. Additionally, the techniques and systems as described herein support real time search through use of keyword signatures.

公开(公告)号：US11663265B2

公开(公告)日：2023-05-30

申请号：US17104745

申请日：2020-11-25

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Shabnam Ghadar ,  Saeid Motiian ,  Ratheesh Kalarot ,  Baldo Faieta ,  Alireza Zaeemzadeh

IPC: G06F16/583 ,  G06F16/532 ,  G06N20/00 ,  G06F16/54 ,  G06F16/56 ,  G06F16/538

CPC classification number: G06F16/583 ,  G06F16/532 ,  G06F16/538 ,  G06F16/54 ,  G06F16/56 ,  G06N20/00

Abstract: A query image is received, along with a query to initiate a search process to find other images based on the query image. The query includes a preference value associated with an attribute, the preference value indicative of a level of emphasis to be placed on the attribute during the search. A full query vector, which is within a first dimensional space and representative of the query image, is generated. The full query vector is projected to a reduced dimensional space having a dimensionality lower than the first dimensional space, to generate a query vector. An attribute direction corresponding to the attribute is identified. A plurality of candidate vectors of the reduced dimensional space is searched, based on the attribute direction, the query vector, and the preference value, to identify a target vector of the plurality of candidate vectors. A target image, representative of the target vector, is displayed.

公开(公告)号：US11604822B2

公开(公告)日：2023-03-14

申请号：US16426369

申请日：2019-05-30

Applicant: Adobe Inc.

Inventor： Pranav Vineet Aggarwal ,  Zhe Lin ,  Baldo Antonio Faieta ,  Saeid Motiian

IPC: G06F7/00 ,  G06F16/583 ,  G06N3/084 ,  G06N20/00 ,  G06F16/538 ,  G06F16/532 ,  G06F16/33 ,  G06F3/04855

Abstract: Multi-modal differential search with real-time focus adaptation techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.