-
公开(公告)号：US20200210763A1
公开(公告)日：2020-07-02
申请号：US16817234
申请日：2020-03-12
Applicant: ADOBE INC.
Inventor： Zhe Lin , Xiaohui Shen , Mingyang Ling , Jianming Zhang , Jason Kuen , Brett Butterfield
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a deep neural network-based model to identify similar digital images for query digital images. For example, the disclosed systems utilize a deep neural network-based model to analyze query digital images to generate deep neural network-based representations of the query digital images. In addition, the disclosed systems can generate results of visually-similar digital images for the query digital images based on comparing the deep neural network-based representations with representations of candidate digital images. Furthermore, the disclosed systems can identify visually similar digital images based on user-defined attributes and image masks to emphasize specific attributes or portions of query digital images.
-
公开(公告)号：US12136185B2
公开(公告)日：2024-11-05
申请号：US17455134
申请日：2021-11-16
Applicant: ADOBE INC.
Inventor： Jason Kuen , Jiuxiang Gu , Zhe Lin
IPC: G06T3/4046 , G06N3/045 , G06N3/08 , G06V10/75
Abstract: Systems and methods for image processing are described. The systems and methods include receiving a low-resolution image; generating a feature map based on the low-resolution image using an encoder of a student network, wherein the encoder of the student network is trained based on comparing a predicted feature map from the encoder of the student network and a fused feature map from a teacher network, and wherein the fused feature map represents a combination of first feature map from a high-resolution encoder of the teacher network and a second feature map from a low-resolution encoder of the teacher network; and decoding the feature map to obtain prediction information for the low-resolution image.
-
公开(公告)号：US11886815B2
公开(公告)日：2024-01-30
申请号：US17333892
申请日：2021-05-28
Applicant: Adobe Inc.
Inventor： Jiuxiang Gu , Vlad Morariu , Varun Manjunatha , Tong Sun , Rajiv Jain , Peizhao Li , Jason Kuen , Handong Zhao
IPC: G06F40/279 , G06F40/205 , G06F16/93 , G06F40/30 , G06N3/088 , G06N3/045
CPC classification number: G06F40/279 , G06F16/93 , G06F40/205 , G06F40/30 , G06N3/045 , G06N3/088
Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.
-
Patent Agency Ranking
公开(公告)号：US20230153943A1
公开(公告)日：2023-05-18
申请号：US17455134
申请日：2021-11-16
Applicant: ADOBE INC.
Inventor： Jason Kuen , Jiuxiang Gu , Zhe Lin
CPC classification number: G06T3/4046 , G06K9/6202 , G06N3/08 , G06N3/0454
Abstract: Systems and methods for image processing are described. The systems and methods include receiving a low-resolution image; generating a feature map based on the low-resolution image using an encoder of a student network, wherein the encoder of the student network is trained based on comparing a predicted feature map from the encoder of the student network and a fused feature map from a teacher network, and wherein the fused feature map represents a combination of first feature map from a high-resolution encoder of the teacher network and a second feature map from a low-resolution encoder of the teacher network; and decoding the feature map to obtain prediction information for the low-resolution image.
-
公开(公告)号：US20220382975A1
公开(公告)日：2022-12-01
申请号：US17333892
申请日：2021-05-28
Applicant: Adobe Inc.
Inventor： Jiuxiang Gu , Vlad Morariu , Varun Manjunatha , Tong Sun , Rajiv Jain , Peizhao Li , Jason Kuen , Handong Zhao
IPC: G06F40/279 , G06N3/04 , G06N3/08 , G06F16/93 , G06F40/30 , G06F40/205
Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.
-
公开(公告)号：US11227185B2
公开(公告)日：2022-01-18
申请号：US16817234
申请日：2020-03-12
Applicant: ADOBE INC.
Inventor： Zhe Lin , Xiaohui Shen , Mingyang Ling , Jianming Zhang , Jason Kuen , Brett Butterfield
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a deep neural network-based model to identify similar digital images for query digital images. For example, the disclosed systems utilize a deep neural network-based model to analyze query digital images to generate deep neural network-based representations of the query digital images. In addition, the disclosed systems can generate results of visually-similar digital images for the query digital images based on comparing the deep neural network-based representations with representations of candidate digital images. Furthermore, the disclosed systems can identify visually similar digital images based on user-defined attributes and image masks to emphasize specific attributes or portions of query digital images.
-
公开(公告)号：US11126890B2
公开(公告)日：2021-09-21
申请号：US16388115
申请日：2019-04-18
Applicant: ADOBE INC.
Inventor： Zhe Lin , Mingyang Ling , Jianming Zhang , Jason Kuen , Federico Perazzi , Brett Butterfield , Baldo Faieta
Abstract: Systems and methods are described for object detection within a digital image using a hierarchical softmax function. The method may include applying a first softmax function of a softmax hierarchy on a digital image based on a first set of object classes that are children of a root node of a class hierarchy, then apply a second (and subsequent) softmax functions to the digital image based on a second (and subsequent) set of object classes, where the second (and subsequent) object classes are children nodes of an object class from the first (or parent) object classes. The methods may then include generating an object recognition output using a convolutional neural network (CNN) based at least in part on applying the first and second (and subsequent) softmax functions. In some cases, the hierarchical softmax function is the loss function for the CNN.
-
公开(公告)号：US20190354802A1
公开(公告)日：2019-11-21
申请号：US15983949
申请日：2018-05-18
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xiaohui Shen , Mingyang Ling , Jianming Zhang , Jason Kuen , Brett Butterfield
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a deep neural network-based model to identify similar digital images for query digital images. For example, the disclosed systems utilize a deep neural network-based model to analyze query digital images to generate deep neural network-based representations of the query digital images. In addition, the disclosed systems can generate results of visually-similar digital images for the query digital images based on comparing the deep neural network-based representations with representations of candidate digital images. Furthermore, the disclosed systems can identify visually similar digital images based on user-defined attributes and image masks to emphasize specific attributes or portions of query digital images.
-
-
-
-
-
-
-
Search Results
8
records
(took 0.023 seconds)
