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：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：US20220391633A1

公开(公告)日：2022-12-08

申请号：US17337194

申请日：2021-06-02

Applicant: Adobe Inc.

Inventor： Midhun Harikumar ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

IPC: G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently generating groups of images portraying semantically similar objects for utilization in building machine learning models. In particular, the disclosed system utilizes metadata and spatial statistics to extract semantically similar objects from a repository of digital images. In some embodiments, the disclosed system generates color embeddings and content embeddings for the identified objects. The disclosed system can further group similar objects together within a query space by utilizing a clustering algorithm to create object clusters and then refining and combining the object clusters within the query space. In some embodiments, the disclosed system utilizes one or more of the object clusters to build a machine learning model.

公开(公告)号：US11436865B1

公开(公告)日：2022-09-06

申请号：US17207178

申请日：2021-03-19

Applicant: Adobe Inc.

Inventor： Saeid Motiian ,  Zhe Lin ,  Shabnam Ghadar ,  Baldo Faieta

IPC: G06V40/16 ,  G06K9/62 ,  G06V30/194 ,  G06V40/10

Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.

公开(公告)号：US20220138247A1

公开(公告)日：2022-05-05

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: G06F16/532 ,  G06F16/583 ,  G06F16/538 ,  G06F16/2457

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：US20220121932A1

公开(公告)日：2022-04-21

申请号：US17384378

申请日：2021-07-23

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Cameron Smith ,  Zhixin Shu ,  Baldo Faieta ,  Shabnam Ghadar ,  Jingwan Lu ,  Aliakbar Darabi ,  Jun-Yan Zhu ,  Niloy Mitra ,  Richard Zhang ,  Elya Shechtman

IPC: G06N3/08 ,  G06N3/04

Abstract: Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

公开(公告)号：US20220121876A1

公开(公告)日：2022-04-21

申请号：US17468498

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Baldo Faieta ,  Shabnam Ghadar

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

Abstract: Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.

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：US11126890B2

公开(公告)日：2021-09-21

申请号：US16388115

申请日：2019-04-18

Applicant: ADOBE INC.

Inventor： Zhe Lin ,  Mingyang Ling ,  Jianming Zhang ,  Jason Kuen ,  Federico Perazzi ,  Brett Butterfield ,  Baldo Faieta

IPC: G06K9/62 ,  G06K9/34 ,  G06N3/08

Abstract: Systems and methods are described for object detection within a digital image using a hierarchical softmax function. The method may include applying a first softmax function of a softmax hierarchy on a digital image based on a first set of object classes that are children of a root node of a class hierarchy, then apply a second (and subsequent) softmax functions to the digital image based on a second (and subsequent) set of object classes, where the second (and subsequent) object classes are children nodes of an object class from the first (or parent) object classes. The methods may then include generating an object recognition output using a convolutional neural network (CNN) based at least in part on applying the first and second (and subsequent) softmax functions. In some cases, the hierarchical softmax function is the loss function for the CNN.

公开(公告)号：US11048779B2

公开(公告)日：2021-06-29

申请号：US14827670

申请日：2015-08-17

Applicant: Adobe Inc.

Inventor： Zeke Koch ,  Baldo Faieta ,  Jen-Chan Chien ,  Mark M. Randall ,  Olivier Sirven ,  Philipp Koch ,  Dennis G. Nicholson

IPC: G06F21/16 ,  G06F21/62 ,  G06F21/10

Abstract: Content creation and licensing control techniques are described. In a first example, a content creation service is configured to support content creation using an image along with functionality to locate the image or a similar image that is available for licensing. In another example, previews of images are used to generate different versions of content along with an option to license images previewed in an approved version of the content. In a further example, fingerprints are used to locate images used as part of content creation by a content creation service without leaving a context of the service. In yet another example, location of licensable versions of images is based at least in part on identification of a watermark included as part of an image. In an additional example, an image itself is used as a basis to locate other images available for licensing by a content sharing service.