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.

公开(公告)号：US11030782B2

公开(公告)日：2021-06-08

申请号：US16679165

申请日：2019-11-09

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Surgan Jandial ,  Abhijeet Kumar ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06T11/40 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.

公开(公告)号：US12124497B1

公开(公告)日：2024-10-22

申请号：US18190686

申请日：2023-03-27

Applicant: Adobe Inc.

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

IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

公开(公告)号：US20240296335A1

公开(公告)日：2024-09-05

申请号：US18112911

申请日：2023-02-22

Applicant: ADOBE INC.

Inventor： Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Balaji Krishnamurthy

IPC: G06N3/096 ,  G06N3/045

CPC classification number: G06N3/096 ,  G06N3/045

Abstract: In various examples, a student model is trained based on a teacher model and a past student model. For example, a first set of labels are generated by a teacher model based on training data, a subset of labels are replace with labels generated by a past student model based on the training data, and a student model it trained based on these labels and the training data.

公开(公告)号：US20240070816A1

公开(公告)日：2024-02-29

申请号：US17823582

申请日：2022-08-31

Applicant: ADOBE INC.

Inventor： Surgan Jandial ,  Siddarth Ramesh ,  Shripad Vilasrao Deshmukh ,  Balaji Krishnamurthy

IPC: G06T5/50 ,  G06T5/00 ,  G06V10/74 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70

CPC classification number: G06T5/50 ,  G06T5/002 ,  G06V10/761 ,  G06V10/764 ,  G06V10/774 ,  G06V20/70 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.

公开(公告)号：US20220237406A1

公开(公告)日：2022-07-28

申请号：US17160862

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 ,  G06F16/535 ,  G06F16/538

Abstract: Techniques are disclosed for text conditioned image searching. A methodology implementing the techniques according to an embodiment includes receiving a source image and a text query defining a target image attribute. The method also includes decomposing the source image into image content and style feature vectors and decomposing the text query into text content and style feature vectors, wherein image style is descriptive of image content and text style is descriptive of text content. The method further includes composing a global content feature vector based on the text content feature vector and the image content feature vector and composing a global style feature vector based on the text style feature vector and the image style feature vector. The method further includes identifying a target image that relates to the global content feature vector and the global style feature vector so that the target image relates to the target image attribute.

公开(公告)号：US20210256387A1

公开(公告)日：2021-08-19

申请号：US16793551

申请日：2020-02-18

Applicant: Adobe Inc.

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

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

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：US20210142539A1

公开(公告)日：2021-05-13

申请号：US16679165

申请日：2019-11-09

Applicant: Adobe Inc.

Inventor： Kumar Ayush ,  Surgan Jandial ,  Abhijeet Kumar ,  Mayur Hemani ,  Balaji Krishnamurthy ,  Ayush Chopra

IPC: G06T11/40 ,  G06T3/00 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a virtual try-on digital image utilizing a unified neural network framework. For example, the disclosed systems can utilize a coarse-to-fine warping process to generate a warped version of a product digital image to fit a model digital image. In addition, the disclosed systems can utilize a texture transfer process to generate a corrected segmentation mask indicating portions of a model digital image to replace with a warped product digital image. The disclosed systems can further generate a virtual try-on digital image based on a warped product digital image, a model digital image, and a corrected segmentation mask. In some embodiments, the disclosed systems can train one or more neural networks to generate accurate outputs for various stages of generating a virtual try-on digital image.