公开(公告)号：US20220121931A1

公开(公告)日：2022-04-21

申请号：US17384371

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 ,  G06K9/62

Abstract: Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector to the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation. The encoder is trained using specialized loss functions including a segmentation loss and a mean latent loss.

公开(公告)号：US20210294834A1

公开(公告)日：2021-09-23

申请号：US16821301

申请日：2020-03-17

Applicant: ADOBE INC.

Inventor： Long Mai ,  Michael Alcorn ,  Baldo Faieta ,  Vladimir Kim

IPC: G06F16/53 ,  G06F16/56 ,  G06K9/62 ,  G06N3/08 ,  G06N20/10

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.

公开(公告)号：US20210217215A1

公开(公告)日：2021-07-15

申请号：US16738359

申请日：2020-01-09

Applicant: Adobe Inc.

Inventor： Kate Sousa ,  Zhe Lin ,  Saeid Motiian ,  Pramod Srinivasan ,  Baldo Faieta ,  Alex Filipkowski

IPC: G06T11/60 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Based on a received digital image and text, a neural network trained to identify candidate text placement areas within images may be used to generate a mask for the digital image that includes a candidate text placement area. A bounding box for the digital image may be defined for the text and based on the candidate text placement area, and the text may be superimposed onto the digital image within the bounding box.

公开(公告)号：US11030236B2

公开(公告)日：2021-06-08

申请号：US15824836

申请日：2017-11-28

Applicant: ADOBE INC.

Inventor： Samarth Gulati ,  Brett Butterfield ,  Baldo Faieta ,  Bernard James Kerr ,  Kent Andrew Edmonds

IPC: G06F16/583 ,  G06F16/532 ,  G06F16/56 ,  G06F16/9535

Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.

公开(公告)号：US10810252B2

公开(公告)日：2020-10-20

申请号：US15002179

申请日：2016-01-20

Applicant: ADOBE INC.

Inventor： Bernard James Kerr ,  Zhe Lin ,  Patrick Reynolds ,  Baldo Faieta

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/532 ,  G06F16/56 ,  G06F16/583 ,  G06N3/08 ,  G06F3/0482 ,  G06F3/0484 ,  G06N5/02

Abstract: In various implementations, specific attributes found in images can be used in a visual-based search. Utilizing machine learning, deep neural networks, and other computer vision techniques, attributes of images, such as color, composition, font, style, and texture can be extracted from a given image. A user can then select a specific attribute from a sample image the user is searching for and the search can be refined to focus on that specific attribute from the sample image. In some embodiments, the search includes specific attributes from more than one image.

公开(公告)号：US20190286932A1

公开(公告)日：2019-09-19

申请号：US15921492

申请日：2018-03-14

Applicant: Adobe Inc.

Inventor： Delun Du ,  Zhe Lin ,  Baldo Faieta

IPC: G06K9/46 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06K9/20

Abstract: The present disclosure is directed toward systems and methods for detecting an object in an input image based on a target object keyword. For example, one or more embodiments described herein generate a heat map of the input image based on the target object keyword and generate various bounding boxes based on a pixel analysis of the heat map. One or more embodiments described herein then utilize the various bounding boxes to determine scores for generated object location proposals in order to provide a highest scoring object location proposal overlaid on the input image.

公开(公告)号：US12254597B2

公开(公告)日：2025-03-18

申请号：US17709221

申请日：2022-03-30

Applicant: Adobe Inc.

Inventor： Cameron Smith ,  Wei-An Lin ,  Timothy M. Converse ,  Shabnam Ghadar ,  Ratheesh Kalarot ,  John Nack ,  Jingwan Lu ,  Hui Qu ,  Elya Shechtman ,  Baldo Faieta

IPC: G06T5/50 ,  G06N3/045

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.

公开(公告)号：US12136189B2

公开(公告)日：2024-11-05

申请号：US17172744

申请日：2021-02-10

Applicant: ADOBE INC.

Inventor： Akhilesh Kumar ,  Zhe Lin ,  Baldo Faieta

IPC: G06T7/00 ,  G06F18/214 ,  G06F18/2411 ,  G06N3/04 ,  G06T5/20

Abstract: The present disclosure describes systems and methods for image enhancement. Embodiments of the present disclosure provide an image enhancement system with a feedback mechanism that provides quantifiable image enhancement information. An image enhancement system may include a discriminator network that determines the quality of the media object. In cases where the discriminator network determines that the media object has a low image quality score (e.g., an image quality score below a quality threshold), the image enhancement system may perform enhancement on the media object using an enhancement network (e.g., using an enhancement network that includes a generative neural network or a generative adversarial network (GAN) model). The discriminator network may then generate an enhancement score for the enhanced media object that may be provided to the user as a feedback mechanism (e.g., where the enhancement score generated by the discriminator network quantifies the enhancement performed by the enhancement network).

公开(公告)号：US12008698B2

公开(公告)日：2024-06-11

申请号：US18117155

申请日：2023-03-03

Applicant: Adobe Inc.

Inventor： Midhun Harikumar ,  Pranav Aggarwal ,  Baldo Faieta ,  Ajinkya Kale ,  Zhe Lin

IPC: G06T11/60 ,  G06T7/11 ,  G06T7/162

CPC classification number: G06T11/60 ,  G06T7/11 ,  G06T7/162 ,  G06T2207/20081 ,  G06T2207/20084

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.

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.