公开(公告)号：US20190138917A1

公开(公告)日：2019-05-09

申请号：US16241456

申请日：2019-01-07

Applicant: Adobe Inc.

Inventor： Balaji Krishnamurthy ,  Tushar Singla

IPC: G06N5/02 ,  G06F16/9535 ,  G06Q30/02 ,  G06F16/901

Abstract: Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.

公开(公告)号：US20250111520A1

公开(公告)日：2025-04-03

申请号：US18478093

申请日：2023-09-29

Applicant: Adobe Inc.

Inventor： Silky Singh ,  Shripad Vilasrao Deshmukh ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06T7/194 ,  G06T7/162 ,  G06T7/246

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that provide self-supervised object discovery systems that combine motion and appearance information to generate segmentation masks from a digital image or digital video and delineate one or more salient objects within the digital image/digital video. The disclosed systems utilize a neural network encoder to generate a fully connected graph based on image patches from the digital input, incorporating image patch feature and optical flow patch feature similarities to produce edge weights. The disclosed systems partition the generated graph to produce a segmentation mask. Furthermore, the disclosed systems iteratively train a segmentation network based on the segmentation mask as a pseudo-ground truth via a bootstrapped, self-training process. By utilizing both motion and appearance information to generate a bi-partitioned graph, the disclosed systems produce high-quality object segmentation masks that represent a foreground and background of digital inputs.

公开(公告)号：US20250005824A1

公开(公告)日：2025-01-02

申请号：US18341982

申请日：2023-06-27

Applicant: ADOBE INC.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Duygu Ceylan Aksit ,  Krishna Kumar Singh ,  Jingwan Lu ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06T11/60 ,  G06T3/00 ,  G06T7/11 ,  G06T11/00

Abstract: Systems and methods for image processing are described. One aspect of the systems and methods includes receiving a plurality of images comprising a first image depicting a first body part and a second image depicting a second body part and encoding, using a texture encoder, the first image and the second image to obtain a first texture embedding and a second texture embedding, respectively. Then, a composite image is generated using a generative decoder, the composite image depicting the first body part and the second body part based on the first texture embedding and the second texture embedding.

公开(公告)号：US20250005812A1

公开(公告)日：2025-01-02

申请号：US18215484

申请日：2023-06-28

Applicant: Adobe Inc.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Mausoom Sarkar ,  Krishna Kumar Singh ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Balaji Krishnamurthy

IPC: G06T11/00 ,  G06T7/12 ,  G06T7/70

Abstract: In implementations of systems for human reposing based on multiple input views, a computing device implements a reposing system to receive input data describing: input digital images; pluralities of keypoints corresponding to the input digital images, the pluralities of keypoints representing poses of a person depicted in the input digital images; and a plurality of keypoints representing a target pose. The reposing system generates selection masks corresponding to the input digital images by processing the input data using a machine learning model. The selection masks represent likelihoods of spatial correspondence between pixels of an output digital image and portions of the input digital images. The reposing system generates the output digital image depicting the person in the target pose for display in a user interface based on the selection masks and the input data.

公开(公告)号：US20240428564A1

公开(公告)日：2024-12-26

申请号：US18213118

申请日：2023-06-22

Applicant: Adobe Inc.

Inventor： Rishabh Jain ,  Mayur Hemani ,  Mausoom Sarkar ,  Krishna Kumar Singh ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Balaji Krishnamurthy

IPC: G06V10/77 ,  G06T3/00 ,  G06T3/40

Abstract: In implementations of systems for generating images for human reposing, a computing device implements a reposing system to receive input data describing an input digital image depicting a person in a first pose, a first plurality of keypoints representing the first pose, and a second plurality of keypoints representing a second pose. The reposing system generates a mapping by processing the input data using a first machine learning model. The mapping indicates a plurality of first portions of the person in the second pose that are visible in the input digital image and a plurality of second portions of the person in the second pose that are invisible in the input digital image. The reposing system generates an output digital image depicting the person in the second pose by processing the mapping, the first plurality of keypoints, and the second plurality of keypoints using a second machine learning model.

公开(公告)号：US12136118B2

公开(公告)日：2024-11-05

申请号：US18186528

申请日：2023-03-20

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Ayush Chopra ,  Patel U. Govind ,  Balaji Krishnamurthy ,  Anirudh Singhal

IPC: G06Q30/00 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045 ,  G06N3/088 ,  G06Q30/0601 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V20/00

Abstract: Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

公开(公告)号：US20240345707A1

公开(公告)日：2024-10-17

申请号：US18409638

申请日：2024-01-10

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Somesh Singh ,  William Brandon George ,  Timothy Chia-chi Liu ,  Suman Basetty ,  Pranjal Prasoon ,  Nikaash Puri ,  Mihir Naware ,  Mihai Corlan ,  Joshua Marshall Butikofer ,  Abhinav Chauhan ,  Kumar Mrityunjay Singh ,  James Patrick O'Reilly ,  Hyman Chung ,  Lauren Dest ,  Clinton Hansen Goudie-Nice ,  Brandon John Pack ,  Balaji Krishnamurthy ,  Kunal Kumar Jain ,  Alexander Klimetschek ,  Matthew William Rozen

IPC: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/20 ,  G06V10/40 ,  G06V10/764

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/206 ,  G06V10/40 ,  G06V10/764 ,  G06T2200/24

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

公开(公告)号：US20240296337A1

公开(公告)日：2024-09-05

申请号：US18178225

申请日：2023-03-03

Applicant: ADOBE INC.

Inventor： Surgan Jandial ,  Tarun Ram Menta ,  Akash Sunil Patil ,  Chirag Agarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06N3/096

CPC classification number: G06N3/096

Abstract: Systems and methods for transfer learning are provided. According to one aspect, a method for transfer learning includes obtaining a target dataset, a source dataset, and a machine learning model trained on the source dataset; selecting a hard subset of the target dataset based on a similarity between the hard subset and the source dataset; computing a transferability metric for the target dataset based on the hard subset of the target dataset; and training the machine learning model using the target dataset based on the transferability metric.

公开(公告)号：US12008033B2

公开(公告)日：2024-06-11

申请号：US17447908

申请日：2021-09-16

Applicant: ADOBE INC.

Inventor： Yaman Kumar ,  Vinh Ngoc Khuc ,  Vijay Srivastava ,  Umang Moorarka ,  Sukriti Verma ,  Simra Shahid ,  Shirsh Bansal ,  Shankar Venkitachalam ,  Sean Steimer ,  Sandipan Karmakar ,  Nimish Srivastav ,  Nikaash Puri ,  Mihir Naware ,  Kunal Kumar Jain ,  Kumar Mrityunjay Singh ,  Hyman Chung ,  Horea Bacila ,  Florin Silviu Iordache ,  Deepak Pai ,  Balaji Krishnamurthy

IPC: G06F7/02 ,  G06F16/00 ,  G06F16/535 ,  G06F16/54 ,  G06F16/58 ,  G06F16/583 ,  G06N20/00

CPC classification number: G06F16/5866 ,  G06F16/535 ,  G06F16/54 ,  G06F16/583 ,  G06N20/00

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

公开(公告)号：US11971884B2

公开(公告)日：2024-04-30

申请号：US17656772

申请日：2022-03-28

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

IPC: G06F16/242 ,  G06F16/248 ,  G06F16/332 ,  G06N20/00 ,  H04L51/02

CPC classification number: G06F16/243 ,  G06F16/2425 ,  G06F16/248 ,  G06F16/3329 ,  G06N20/00 ,  H04L51/02

Abstract: An interactive search session is implemented using an artificial intelligence model. For example, when the artificial intelligence model receives a search query from a user, the model selects an action from a plurality of actions based on the search query. The selected action queries the user for more contextual cues about the search query (e.g., may enquire about use of the search results, may request to refine the search query, or otherwise engage the user in conversation to better understand the intent of the search). The interactive search session may be in the form, for example, of a chat session between the user and the system, and the chat session may be displayed along with the search results (e.g., in a separate section of display). The interactive search session may enable the system to better understand the user's search needs, and accordingly may help provide more focused search results.