公开(公告)号：US12106603B2

公开(公告)日：2024-10-01

申请号：US17454645

申请日：2021-11-12

Applicant: ADOBE INC.

Inventor： Pinkesh Badjatiya ,  Parth Patel

IPC: G06K9/00 ,  G06F3/04847 ,  G06T11/60 ,  G06V40/16 ,  G06N3/02

CPC classification number: G06V40/161 ,  G06F3/04847 ,  G06T11/60 ,  G06V40/168 ,  G06N3/02 ,  G06T2200/24

Abstract: Systems and methods for image processing are described. One or more embodiments of the method, apparatus, non-transitory computer readable medium, and system include identifying an encoding of an image, an attribute to be modified in the image, and a plurality of attributes to be preserved in the image; generating a non-linear interpolation for the encoding by iteratively identifying a sequence of boundary vectors, wherein each boundary vector of the sequence of boundary vectors is based on selecting a plurality of conditional boundary vectors representing a subset of the plurality of attributes to be preserved at each corresponding iteration; and generating a modified image based on the image encoding and the non-linear interpolation, wherein the modified image corresponds to the image with the attribute to be modified.

公开(公告)号：US11907816B2

公开(公告)日：2024-02-20

申请号：US17892878

申请日：2022-08-22

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

IPC: G06N20/00 ,  G06N20/10 ,  G06F18/2431 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2453

CPC classification number: G06N20/00 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2431 ,  G06F18/2453 ,  G06N20/10

Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

公开(公告)号：US12190061B2

公开(公告)日：2025-01-07

申请号：US17644856

申请日：2021-12-17

Applicant: ADOBE INC.

Inventor： Shashank Shailabh ,  Madhur Panwar ,  Milan Aggarwal ,  Pinkesh Badjatiya ,  Simra Shahid ,  Nikaash Puri ,  S Sejal Naidu ,  Sharat Chandra Racha ,  Balaji Krishnamurthy ,  Ganesh Karbhari Palwe

IPC: G06F40/289 ,  G06F40/30 ,  G06F40/40

Abstract: Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.

公开(公告)号：US11874902B2

公开(公告)日：2024-01-16

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: G06F18/25 ,  G06N3/04 ,  G06F16/538 ,  G06F16/532 ,  G06F16/535 ,  G06F18/214

CPC classification number: G06F18/253 ,  G06F16/532 ,  G06F16/535 ,  G06F16/538 ,  G06F18/214 ,  G06F18/251 ,  G06N3/04

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：US11948358B2

公开(公告)日：2024-04-02

申请号：US17455126

申请日：2021-11-16

Applicant: ADOBE INC.

Inventor： Sumegh Roychowdhury ,  Sumedh A. Sontakke ,  Mausoom Sarkar ,  Nikaash Puri ,  Pinkesh Badjatiya ,  Milan Aggarwal

IPC: G06V20/40 ,  G06N3/088

CPC classification number: G06V20/41 ,  G06N3/088 ,  G06V20/47 ,  G06V20/44

Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

公开(公告)号：US20230196191A1

公开(公告)日：2023-06-22

申请号：US17892878

申请日：2022-08-22

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

IPC: G06N20/00 ,  G06N20/10 ,  G06F18/2431 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2453

CPC classification number: G06N20/00 ,  G06N20/10 ,  G06F18/2431 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2453

Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

公开(公告)号：US11423264B2

公开(公告)日：2022-08-23

申请号：US16659147

申请日：2019-10-21

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

IPC: G06K9/62 ,  G06N20/10

Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.

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：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：US20210117718A1

公开(公告)日：2021-04-22

申请号：US16659147

申请日：2019-10-21

Applicant: Adobe Inc.

Inventor： Pinkesh Badjatiya ,  Nikaash Puri ,  Ayush Chopra ,  Anubha Kabra

IPC: G06K9/62 ,  G06N20/10

Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.