公开(公告)号：US11216505B2

公开(公告)日：2022-01-04

申请号：US16561973

申请日：2019-09-05

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

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：US20210073270A1

公开(公告)日：2021-03-11

申请号：US16561973

申请日：2019-09-05

Applicant: Adobe Inc.

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

IPC: G06F16/583 ,  G06F16/532 ,  G06F16/55 ,  G06F17/16

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：US20200380403A1

公开(公告)日：2020-12-03

申请号：US16426298

申请日：2019-05-30

Applicant: Adobe Inc.

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

IPC: G06N20/00 ,  G06K9/62 ,  G06F16/538 ,  G06N3/08

Abstract: Visually guided machine-learning language model and embedding 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：US11853348B2

公开(公告)日：2023-12-26

申请号：US16910440

申请日：2020-06-24

Applicant: Adobe Inc.

Inventor： Akhilesh Kumar ,  Zhe Lin ,  Ratheesh Kalarot ,  Jinrong Xie ,  Jianming Zhang ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06F16/532 ,  G06F16/583 ,  G06F16/55 ,  G06F16/538 ,  G06N3/02 ,  G06N20/20

CPC classification number: G06F16/532 ,  G06F16/538 ,  G06F16/55 ,  G06F16/583 ,  G06N3/02 ,  G06N20/20

Abstract: Multidimensional digital content search techniques are described that support an ability of a computing device to perform search with increased granularity and flexibility over conventional techniques. In one example, a control is implemented by a computing device that defines a multidimensional (e.g., two-dimensional) continuous space. Locations in the multidimensional continuous space are usable to different search criteria through different weights applied to the criteria associated with the axes. Therefore, user interaction with this control may be used to define a location and corresponding coordinates that may act as weights to the search criteria in order to perform a search of digital content through use of a single user input.

公开(公告)号：US20230360299A1

公开(公告)日：2023-11-09

申请号：US18224916

申请日：2023-07-21

Applicant: Adobe Inc.

Inventor： Yang Yang ,  Zhixin Shu ,  Shabnam Ghadar ,  Jingwan Lu ,  Jakub Fiser ,  Elya Schechtman ,  Cameron Y. Smith ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06T11/60 ,  G06F21/62 ,  G06F16/56 ,  G06F16/532

CPC classification number: G06T11/60 ,  G06F21/6254 ,  G06F16/56 ,  G06F16/532 ,  G06T2200/24

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

公开(公告)号：US20220148243A1

公开(公告)日：2022-05-12

申请号：US17094093

申请日：2020-11-10

Applicant: Adobe Inc.

Inventor： Yang Yang ,  Zhixin Shu ,  Shabnam Ghadar ,  Jingwan Lu ,  Jakub Fiser ,  Elya Schechtman ,  Cameron Y. Smith ,  Baldo Antonio Faieta ,  Alex Charles Filipkowski

IPC: G06T11/60 ,  G06T9/00 ,  G06F3/0484 ,  G06F16/532 ,  G06F21/62 ,  G06F16/56 ,  G06N20/00

Abstract: Face anonymization techniques are described that overcome conventional challenges to generate an anonymized face. In one example, a digital object editing system is configured to generate an anonymized face based on a target face and a reference face. As part of this, the digital object editing system employs an encoder as part of machine learning to extract a target encoding of the target face image and a reference encoding of the reference face. The digital object editing system then generates a mixed encoding from the target and reference encodings. The mixed encoding is employed by a machine-learning model of the digital object editing system to generate a mixed face. An object replacement module is used by the digital object editing system to replace the target face in the target digital image with the mixed face.

公开(公告)号：US20220121705A1

公开(公告)日：2022-04-21

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: G06F16/583 ,  G06F17/16 ,  G06F16/55 ,  G06F16/532

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：US20210365727A1

公开(公告)日：2021-11-25

申请号：US17398317

申请日：2021-08-10

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06F16/535 ,  G06N20/00 ,  G06K9/72

Abstract: Text-to-visual machine learning embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. These techniques include use of query-based training data which may expand availability and types of training data usable to train a model. Generation of negative digital image samples is also described that may increase accuracy in training the model using machine learning. A loss function is also described that also supports increased accuracy and computational efficiency by losses separately, e.g., between positive or negative sample embeddings a text embedding.

公开(公告)号：US11144784B2

公开(公告)日：2021-10-12

申请号：US16426264

申请日：2019-05-30

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06K9/72 ,  G06F16/535 ,  G06N20/00 ,  G06F3/0482

Abstract: Text-to-visual machine learning embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. These techniques include use of query-based training data which may expand availability and types of training data usable to train a model. Generation of negative digital image samples is also described that may increase accuracy in training the model using machine learning. A loss function is also described that also supports increased accuracy and computational efficiency by losses separately, e.g., between positive or negative sample embeddings a text embedding.

公开(公告)号：US20200380298A1

公开(公告)日：2020-12-03

申请号：US16426264

申请日：2019-05-30

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06K9/72 ,  G06F16/535 ,  G06N20/00

Abstract: Text-to-visual machine learning embedding techniques are described that overcome the challenges of conventional techniques in a variety of ways. These techniques include use of query-based training data which may expand availability and types of training data usable to train a model. Generation of negative digital image samples is also described that may increase accuracy in training the model using machine learning. A loss function is also described that also supports increased accuracy and computational efficiency by losses separately, e.g., between positive or negative sample embeddings a text embedding.