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：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：US20240119122A1

公开(公告)日：2024-04-11

申请号：US18045542

申请日：2022-10-11

Applicant: ADOBE INC.

Inventor： Shripad Vilasrao Deshmukh ,  Surgan Jandial ,  Abhinav Java ,  Milan Aggarwal ,  Mausoom Sarkar ,  Arneh Jain ,  Balaji Krishnamurthy

IPC: G06K9/62

CPC classification number: G06K9/6269 ,  G06K9/6259 ,  G06K9/6285

Abstract: Systems and methods for data augmentation are provided. One aspect of the systems and methods include receiving an image that is misclassified by a classification network; computing an augmentation image based on the image using an augmentation network; and generating an augmented image by combining the image and the augmentation image, wherein the augmented image is correctly classified by the classification network.

公开(公告)号：US11797823B2

公开(公告)日：2023-10-24

申请号：US16793551

申请日：2020-02-18

Applicant: Adobe Inc.

Inventor： Ayush Chopra ,  Balaji Krishnamurthy ,  Mausoom Sarkar ,  Surgan Jandial

IPC: G06N3/04 ,  G06N3/084 ,  G06F18/214 ,  G06N3/047 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/04 ,  G06F18/214 ,  G06N3/047 ,  G06N3/084 ,  G06V10/764 ,  G06V10/82

Abstract: Generating a machine learning model that is trained using retrospective loss is described. A retrospective loss system receives an untrained machine learning model and a task for training the model. The retrospective loss system initially trains the model over warm-up iterations using task-specific loss that is determined based on a difference between predictions output by the model during training on input data and a ground truth dataset for the input data. Following the warm-up training iterations, the retrospective loss system continues to train the model using retrospective loss, which is model-agnostic and constrains the model such that a subsequently output prediction is more similar to the ground truth dataset than the previously output prediction. After determining that the model's outputs are within a threshold similarity to the ground truth dataset, the model is output with its current parameters as a trained model.

公开(公告)号：US20230267345A1

公开(公告)日：2023-08-24

申请号：US18135948

申请日：2023-04-18

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06N5/04 ,  G06N20/00 ,  G06N3/08 ,  G06V10/82 ,  G06N20/10

CPC classification number: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

公开(公告)号：US11734337B2

公开(公告)日：2023-08-22

申请号：US17806922

申请日：2022-06-14

Applicant: Adobe Inc.

Inventor： Ayush Chopra ,  Mausoom Sarkar ,  Jonas Dahl ,  Hiresh Gupta ,  Balaji Krishnamurthy ,  Abhishek Sinha

IPC: G06F16/532 ,  G06F16/535 ,  G06F17/15 ,  G06N3/04 ,  G06F16/55 ,  G06F18/22 ,  G06F18/24 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44

CPC classification number: G06F16/532 ,  G06F16/535 ,  G06F16/55 ,  G06F17/15 ,  G06F18/22 ,  G06F18/24 ,  G06N3/04 ,  G06V10/454 ,  G06V10/764 ,  G06V10/82

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.

公开(公告)号：US11720651B2

公开(公告)日：2023-08-08

申请号：US17160893

申请日：2021-01-28

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Surgan Jandial ,  Pranit Chawla ,  Mausoom Sarkar ,  Ayush Chopra

IPC: G06F18/25 ,  G06N3/04 ,  G06F16/583 ,  G06F16/532 ,  G06F16/538 ,  G06F18/214

CPC classification number: G06F18/253 ,  G06F16/532 ,  G06F16/538 ,  G06F16/5846 ,  G06F18/214 ,  G06F18/251 ,  G06N3/04

Abstract: Techniques are disclosed for text-conditioned image searching. A methodology implementing the techniques includes decomposing a source image into visual feature vectors associated with different levels of granularity. The method also includes decomposing a text query (defining a target image attribute) into feature vectors associated with different levels of granularity including a global text feature vector. The method further includes generating image-text embeddings based on the visual feature vectors and the text feature vectors to encode information from visual and textual features. The method further includes composing a visio-linguistic representation based on a hierarchical aggregation of the image-text embeddings to encode visual and textual information at multiple levels of granularity. The method further includes identifying a target image that includes the visio-linguistic representation and the global text feature vector, so that the target image relates to the target image attribute, and providing the target image as an image search result.

公开(公告)号：US11657306B2

公开(公告)日：2023-05-23

申请号：US16904263

申请日：2020-06-17

Applicant: Adobe Inc.

Inventor： Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy

IPC: G06N5/04 ,  G06N20/00 ,  G06N3/08 ,  G06V10/82 ,  G06N20/10

CPC classification number: G06N5/04 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06V10/82

Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.

公开(公告)号：US20230134460A1

公开(公告)日：2023-05-04

申请号：US17517434

申请日：2021-11-02

Applicant: Adobe Inc.

Inventor： Shripad Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Hiresh Gupta

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/72 ,  G06K9/46 ,  G06N3/08

Abstract: In implementations of refining element associations for form structure extraction, a computing device implements a structure system to receive estimate data describing estimated associations of elements included in a form and a digital image depicting the form. An image patch is extracted from the digital image, and the image patch depicts a pair of elements of the elements included in the form. The structure system encodes an indication of whether the pair of elements have an association of the estimated associations. An indication is generated that the pair of elements have a particular association based at least partially on the encoded indication, bounding boxes of the pair of elements, and text depicted in the image patch.

公开(公告)号：US20220245391A1

公开(公告)日：2022-08-04

申请号：US17160893

申请日：2021-01-28

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Surgan Jandial ,  Pranit Chawla ,  Mausoom Sarkar ,  Ayush Chopra

IPC: G06K9/62 ,  G06N3/04 ,  G06F16/532 ,  G06F16/538 ,  G06F16/583

Abstract: Techniques are disclosed for text-conditioned image searching. A methodology implementing the techniques includes decomposing a source image into visual feature vectors associated with different levels of granularity. The method also includes decomposing a text query (defining a target image attribute) into feature vectors associated with different levels of granularity including a global text feature vector. The method further includes generating image-text embeddings based on the visual feature vectors and the text feature vectors to encode information from visual and textual features. The method further includes composing a visio-linguistic representation based on a hierarchical aggregation of the image-text embeddings to encode visual and textual information at multiple levels of granularity. The method further includes identifying a target image that includes the visio-linguistic representation and the global text feature vector, so that the target image relates to the target image attribute, and providing the target image as an image search result.