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公开(公告)号：US11030236B2
公开(公告)日：2021-06-08
申请号：US15824836
申请日：2017-11-28
Applicant: ADOBE INC.
Inventor： Samarth Gulati , Brett Butterfield , Baldo Faieta , Bernard James Kerr , Kent Andrew Edmonds
IPC: G06F16/583 , G06F16/532 , G06F16/56 , G06F16/9535
Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.
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Patent Agency Ranking
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.
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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.
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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.
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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.
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公开(公告)号：US20190163766A1
公开(公告)日：2019-05-30
申请号：US15824836
申请日：2017-11-28
Applicant: ADOBE INC.
Inventor： Samarth Gulati , Brett Butterfield , Baldo Faieta , Bernard James Kerr , Kent Andrew Edmonds
IPC: G06F17/30
Abstract: Systems and methods for searching digital content, such as digital images, are disclosed. A method includes receiving a first search constraint and generating search results based on the first search constraint. A search constraint includes search values or criteria. The search results include a ranked set of digital images. A second search constraint and a weight value associated with the second search constraint are received. The search results are updated based on the second search constraint and the weight value. The updated search results are provided to a user. Updating the search results includes determining a ranking (or a re-ranking) for each item of content included in the search results based on the first search constraint, the second search constraint, and the weight value. Re-ranking the search results may further be based on a weight value associated with the first search constraint, such as a default or maximum weight value.
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