公开(公告)号：US11756239B2

公开(公告)日：2023-09-12

申请号：US17240030

申请日：2021-04-26

Applicant: ADOBE INC.

Inventor： Pranav Aggarwal ,  Ajinkya Kale

IPC: G06T11/00 ,  G06T7/90 ,  G06T7/11

CPC classification number: G06T11/001 ,  G06T7/11 ,  G06T7/90

Abstract: Systems and methods for color replacement are described. Embodiments of the disclosure include a color replacement system that adjusts an image based on a user-input source color and target color. For example, the source color may be replaced with the target color throughout the entire image. In some embodiments, a user provides a speech or text input that identifies a source color to be replaced. The user may then provide a speech or text input identifying the target color, replacing the source color. A color replacement system creates and embedding of the source color, segments the image based on the source color embedding, and then replaces the color of segmented portion of the image with the target color.

公开(公告)号：US11645478B2

公开(公告)日：2023-05-09

申请号：US17088847

申请日：2020-11-04

Applicant: Adobe Inc.

Inventor： Ritiz Tambi ,  Pranav Aggarwal ,  Ajinkya Kale

IPC: G06F40/58 ,  G06F40/117

CPC classification number: G06F40/58 ,  G06F40/117

Abstract: Introduced here is an approach to translating tags assigned to digital images. As an example, embeddings may be extracted from a tag to be translated and the digital image with which the tag is associated by a multimodal model. These embeddings can be compared to embeddings extracted from a set of target tags associated with a target language by the multimodal model. Such an approach allows similarity to be established along two dimensions, which ensures the obstacles associated with direct translation can be avoided.

公开(公告)号：US11615567B2

公开(公告)日：2023-03-28

申请号：US16952008

申请日：2020-11-18

Applicant: Adobe Inc.

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

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

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, by a model that includes trainable components, a learned image representation of a target image. The operations further include generating, by 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 generating a class activation map of the target image by, at least, 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 using the class activation map of the target image.

公开(公告)号：US11615239B2

公开(公告)日：2023-03-28

申请号：US16836462

申请日：2020-03-31

Applicant: Adobe Inc.

Inventor： Oliver Brdiczka ,  Ajinkya Kale ,  Piyush Chandra ,  Tracy King ,  Abhishek Gupta ,  Sourabh Goel ,  Nitin Garg ,  Deepika Naryani ,  Feroz Ahmad ,  Vikas Sagar

IPC: G06F15/16 ,  G06F9/54 ,  H04L29/06 ,  G06F40/20 ,  G06F40/40 ,  G06F40/35 ,  H04L51/00

Abstract: The present disclosure relates to systems for identifying instances of natural language input, determining intent classifications associated with instances of natural language input, and generating responses based on the determined intent classifications. In particular, the disclosed systems intelligently identify and group instances of natural language input based on characteristics of the user input. Additionally, the disclosed systems determine intent classifications for the instances of natural language input based message queuing in order to delay responses to the user input in ways that increase accuracy of the responses, while retaining a conversational aspect of the ongoing chat. Moreover, in one or more embodiments, the disclosed systems generate responses utilizing natural language.

公开(公告)号：US20220284321A1

公开(公告)日：2022-09-08

申请号：US17190668

申请日：2021-03-03

Applicant: ADOBE INC.

Inventor： Xin Yuan ,  Zhe Lin ,  Jason Wen Yong Kuen ,  Jianming Zhang ,  Yilin Wang ,  Ajinkya Kale ,  Baldo Faieta

IPC: G06N5/04 ,  G06N20/00

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

公开(公告)号：US20220277039A1

公开(公告)日：2022-09-01

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 ,  G06K9/46 ,  G06F16/532 ,  G06F16/51 ,  G06F16/538 ,  G06N3/08

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：US11232147B2

公开(公告)日：2022-01-25

申请号：US16525366

申请日：2019-07-29

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/20 ,  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.