Patents by Inventor Baldo Faieta

Baldo Faieta has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11983628
    Abstract: Systems and methods dynamically adjust an available range for editing an attribute in an image. An image editing system computes a metric for an attribute in an input image as a function of a latent space representation of the input image and a filtering vector for editing the input image. The image editing system compares the metric to a threshold. If the metric exceeds the threshold, then the image editing system selects a first range for editing the attribute in the input image. If the metric does not exceed the threshold, a second range is selected. The image editing system causes display of a user interface for editing the input image comprising an interface element for editing the attribute within the selected range.
    Type: Grant
    Filed: September 7, 2021
    Date of Patent: May 14, 2024
    Assignee: Adobe Inc.
    Inventors: Wei-An Lin, Baldo Faieta, Cameron Smith, Elya Shechtman, Jingwan Lu, Jun-Yan Zhu, Niloy Mitra, Ratheesh Kalarot, Richard Zhang, Shabnam Ghadar, Zhixin Shu
  • Patent number: 11928762
    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for editing images using a web-based intermediary between a user interface on a client device and an image editing neural network(s) (e.g., a generative adversarial network) on a server(s). The present image editing system supports multiple users in the same software container, advanced concurrency of projection and transformation of the same image, clubbing transformation requests from several users hosted in the same software container, and smooth display updates during a progressive projection.
    Type: Grant
    Filed: September 3, 2021
    Date of Patent: March 12, 2024
    Assignee: Adobe Inc.
    Inventors: Akhilesh Kumar, Ratheesh Kalarot, Baldo Faieta, Shabnam Ghadar
  • Patent number: 11915520
    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.
    Type: Grant
    Filed: September 2, 2022
    Date of Patent: February 27, 2024
    Assignee: Adobe Inc.
    Inventors: Saeid Motiian, Zhe Lin, Shabnam Ghadar, Baldo Faieta
  • Patent number: 11914641
    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.
    Type: Grant
    Filed: February 26, 2021
    Date of Patent: February 27, 2024
    Assignee: ADOBE INC.
    Inventors: Pranav Aggarwal, Ajinkya Kale, Baldo Faieta, Saeid Motiian, Venkata naveen kumar yadav Marri
  • Patent number: 11907280
    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.
    Type: Grant
    Filed: November 5, 2020
    Date of Patent: February 20, 2024
    Assignee: Adobe Inc.
    Inventors: Mikhail Kotov, Roland Geisler, Saeid Motiian, Dylan Nathaniel Warnock, Michele Saad, Venkata Naveen Kumar Yadav Marri, Ajinkya Kale, Ryan Rozich, Baldo Faieta
  • Patent number: 11886793
    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.
    Type: Grant
    Filed: September 3, 2021
    Date of Patent: January 30, 2024
    Assignee: Adobe Inc.
    Inventors: Zhaowen Wang, Saeid Motiian, Baldo Faieta, Zegi Gu, Peter Evan O'Donovan, Alex Filipkowski, Jose Ignacio Echevarria Vallespi
  • Patent number: 11887216
    Abstract: The present disclosure describes systems and methods for image processing. Embodiments of the present disclosure include an image processing apparatus configured to generate modified images (e.g., synthetic faces) by conditionally changing attributes or landmarks of an input image. A machine learning model of the image processing apparatus encodes the input image to obtain a joint conditional vector that represents attributes and landmarks of the input image in a vector space. The joint conditional vector is then modified, according to the techniques described herein, to form a latent vector used to generate a modified image. In some cases, the machine learning model is trained using a generative adversarial network (GAN) with a normalization technique, followed by joint training of a landmark embedding and attribute embedding (e.g., to reduce inference time).
    Type: Grant
    Filed: November 19, 2021
    Date of Patent: January 30, 2024
    Assignee: ADOBE, INC.
    Inventors: Ratheesh Kalarot, Timothy M. Converse, Shabnam Ghadar, John Thomas Nack, Jingwan Lu, Elya Shechtman, Baldo Faieta, Akhilesh Kumar
  • Patent number: 11875221
    Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.
    Type: Grant
    Filed: September 7, 2021
    Date of Patent: January 16, 2024
    Assignee: Adobe Inc.
    Inventors: Wei-An Lin, Baldo Faieta, Cameron Smith, Elya Shechtman, Jingwan Lu, Jun-Yan Zhu, Niloy Mitra, Ratheesh Kalarot, Richard Zhang, Shabnam Ghadar, Zhixin Shu
  • Patent number: 11854119
    Abstract: Embodiments are disclosed for automatic object re-colorization in images. In some embodiments, a method of automatic object re-colorization includes receiving a request to recolor an object in an image, the request including an object identifier and a color identifier, identifying an object in the image associated with the object identifier, generating a mask corresponding to the object in the image, providing the image, the mask, and the color identifier to a color transformer network, the color transformer network trained to recolor objects in input images, and generating, by the color transformer network, a recolored image, wherein the object in the recolored image has been recolored to a color corresponding to the color identifier.
    Type: Grant
    Filed: January 22, 2021
    Date of Patent: December 26, 2023
    Assignee: Adobe Inc.
    Inventors: Siavash Khodadadeh, Zhe Lin, Shabnam Ghadar, Saeid Motiian, Richard Zhang, Ratheesh Kalarot, Baldo Faieta
  • Publication number: 20230386114
    Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.
    Type: Application
    Filed: August 14, 2023
    Publication date: November 30, 2023
    Inventors: Akhilesh Kumar, Baldo Faieta, Piotr Walczyszyn, Ratheesh Kalarot, Archie Bagnall, Shabnam Ghadar, Wei-An Lin, Cameron Smith, Christian Cantrell, Patrick Hebron, Wilson Chan, Jingwan Lu, Holger Winnemoeller, Sven Olsen
  • Patent number: 11823490
    Abstract: Systems and methods for image processing are described. One or more embodiments of the present disclosure identify a latent vector representing an image of a face, identify a target attribute vector representing a target attribute for the image, generate a modified latent vector using a mapping network that converts the latent vector and the target attribute vector into a hidden representation having fewer dimensions than the latent vector, wherein the modified latent vector is generated based on the hidden representation, and generate a modified image based on the modified latent vector, wherein the modified image represents the face with the target attribute.
    Type: Grant
    Filed: June 8, 2021
    Date of Patent: November 21, 2023
    Assignee: ADOBE, INC.
    Inventors: Ratheesh Kalarot, Siavash Khodadadeh, Baldo Faieta, Shabnam Ghadar, Saeid Motiian, Wei-An Lin, Zhe Lin
  • Publication number: 20230316475
    Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.
    Type: Application
    Filed: March 30, 2022
    Publication date: October 5, 2023
    Inventors: Cameron Smith, Wei-An Lin, Timothy M. Converse, Shabnam Ghadar, Ratheesh Kalarot, John Nack, Jingwan Lu, Hui Qu, Elya Shechtman, Baldo Faieta
  • Publication number: 20230316606
    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for latent-based editing of digital images using a generative neural network. In particular, in one or more embodiments, the disclosed systems perform latent-based editing of a digital image by mapping a feature tensor and a set of style vectors for the digital image into a joint feature style space. In one or more implementations, the disclosed systems apply a joint feature style perturbation and/or modification vectors within the joint feature style space to determine modified style vectors and a modified feature tensor. Moreover, in one or more embodiments the disclosed systems generate a modified digital image utilizing a generative neural network from the modified style vectors and the modified feature tensor.
    Type: Application
    Filed: March 21, 2022
    Publication date: October 5, 2023
    Inventors: Hui Qu, Baldo Faieta, Cameron Smith, Elya Shechtman, Jingwan Lu, Ratheesh Kalarot, Richard Zhang, Saeid Motiian, Shabnam Ghadar, Wei-An Lin
  • Patent number: 11762622
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for remotely generating modified digital images utilizing an interactive image editing architecture. For example, the disclosed systems receive an image editing request for remotely editing a digital image utilizing an interactive image editing architecture. In some cases, the disclosed systems maintain, via a canvas worker container, a digital stream that reflects versions of the digital image. The disclosed systems determine, from the digital stream utilizing the canvas worker container, an image differential metric indicating a difference between a first version of the digital image and a second version of the digital image associated with the image editing request. Further, the disclosed systems provide the image differential metric to a client device for rendering the second version of the digital image to reflect a modification corresponding to the user interaction.
    Type: Grant
    Filed: May 16, 2022
    Date of Patent: September 19, 2023
    Assignee: Adobe Inc.
    Inventors: Sven Olsen, Shabnam Ghadar, Baldo Faieta, Akhilesh Kumar
  • Patent number: 11741157
    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.
    Type: Grant
    Filed: December 7, 2021
    Date of Patent: August 29, 2023
    Assignee: Adobe Inc.
    Inventors: Ajinkya Kale, Baldo Faieta, Benjamin Leviant, Fengbin Chen, Francois Guerin, Kate Sousa, Trung Bui, Venkat Barakam, Zhe Lin
  • Patent number: 11727614
    Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for detecting user interactions to edit a digital image from a client device and modify the digital image for the client device by using a web-based intermediary that modifies a latent vector of the digital image and an image modification neural network to generate a modified digital image from the modified latent vector. In response to user interaction to modify a digital image, for instance, the disclosed systems modify a latent vector extracted from the digital image to reflect the requested modification. The disclosed systems further use a latent vector stream renderer (as an intermediary device) to generate an image delta that indicates a difference between the digital image and the modified digital image. The disclosed systems then provide the image delta as part of a digital stream to a client device to quickly render the modified digital image.
    Type: Grant
    Filed: February 23, 2021
    Date of Patent: August 15, 2023
    Assignee: Adobe Inc.
    Inventors: Akhilesh Kumar, Baldo Faieta, Piotr Walczyszyn, Ratheesh Kalarot, Archie Bagnall, Shabnam Ghadar, Wei-An Lin, Cameron Smith, Christian Cantrell, Patrick Hebron, Wilson Chan, Jingwan Lu, Holger Winnemoeller, Sven Olsen
  • Patent number: 11709885
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image.
    Type: Grant
    Filed: September 18, 2020
    Date of Patent: July 25, 2023
    Assignee: Adobe Inc.
    Inventors: John Collomosse, Zhe Lin, Saeid Motiian, Hailin Jin, Baldo Faieta, Alex Filipkowski
  • Publication number: 20230206525
    Abstract: A non-transitory computer-readable medium includes program code that is stored thereon. The program code is executable by one or more processing devices for performing operations including generating, using a model, a learned image representation of a target image. The operations further include generating, using a text embedding model, a text embedding of a text query. The text embedding and the learned image representation of the target image are in a same embedding space. Additionally, the operations include convolving the learned image representation of the target image with the text embedding of the text query. Moreover, the operations include generating an object-segmented image based on the convolving of the learned image representation of the target image with the text embedding.
    Type: Application
    Filed: March 3, 2023
    Publication date: June 29, 2023
    Inventors: Midhun Harikumar, Pranav Aggarwal, Baldo Faieta, Ajinkya Kale, Zhe Lin
  • Patent number: 11663265
    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.
    Type: Grant
    Filed: November 25, 2020
    Date of Patent: May 30, 2023
    Assignee: Adobe Inc.
    Inventors: Zhe Lin, Shabnam Ghadar, Saeid Motiian, Ratheesh Kalarot, Baldo Faieta, Alireza Zaeemzadeh
  • Patent number: 11645328
    Abstract: Systems and methods for performing image search are described. An image search method may include generating a feature vector for each of a plurality of stored images using a machine learning model trained using a rotation loss term, receiving a search query comprising a search image with object having an orientation, generating a query feature vector for the search image using the machine learning model, wherein the query feature vector is based at least in part on the orientation, comparing the query feature vector to the feature vector for each of the plurality of stored images, and selecting at least one stored image of the plurality of stored images based on the comparison, wherein the at least one stored image comprises a similar orientation to the orientation of the object in the search image.
    Type: Grant
    Filed: March 17, 2020
    Date of Patent: May 9, 2023
    Assignee: ADOBE INC.
    Inventors: Long Mai, Michael Alcorn, Baldo Faieta, Vladimir Kim