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公开(公告)号：US20220083809A1
公开(公告)日：2022-03-17
申请号：US17021279
申请日：2020-09-15
Applicant: Adobe Inc.
Inventor： Arshiya Aggarwal , Sanjeev Tagra , Sachin Soni , Ryan Rozich , Prasenjit Mondal , Jonathan Roeder , Ajay Jain
Abstract: An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.
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公开(公告)号：US11836850B2
公开(公告)日：2023-12-05
申请号：US17336109
申请日：2021-06-01
Applicant: Adobe Inc.
Inventor： Sanjeev Tagra , Sachin Soni , Ajay Jain , Ryan Rozich , Jonathan Roeder , Prasenjit Mondal
Abstract: Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.
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公开(公告)号：US11748451B2
公开(公告)日：2023-09-05
申请号：US17021279
申请日：2020-09-15
Applicant: Adobe Inc.
Inventor： Arshiya Aggarwal , Sanjeev Tagra , Sachin Soni , Ryan Rozich , Prasenjit Mondal , Jonathan Roeder , Ajay Jain
CPC classification number: G06F18/22 , G06F18/2113 , G06N5/04 , G06T5/50 , G06V10/40 , G06T2207/20081 , G06T2207/20084
Abstract: An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.
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公开(公告)号：US20220277368A1
公开(公告)日：2022-09-01
申请号：US17186495
申请日：2021-02-26
Applicant: Adobe Inc.
Inventor： Sanjeev Tagra , Sachin Soni , Ryan Rozich , Nitish Maurya , Jonathan Roeder , Ajay Jain , Ajay Bedi
IPC: G06Q30/06 , G06N3/04 , G06N3/08 , G06K9/62 , G06F3/0484
Abstract: Techniques are disclosed for generating image recommendations to facilitate the sale of a product. An example methodology includes identifying a product category associated with an image of the product provided by the seller, and a product sub-category associated with the product image. The method further includes retrieving one or more images of for-sale items. The retrieval is based on a search of for-sale listings using the identified product category and the identified product sub-category. The method further includes clustering the retrieved images of for-sale items into groups, each group associated with a perspective viewpoint of the for-sale item. The method further includes providing a selected image from each group as an image recommendation. The selection is based on a value score associated with each of the images of the for-sale items. A graphical status indicating completeness of the seller's image set is updated in response to recommended images being adopted.
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公开(公告)号：US20210287425A1
公开(公告)日：2021-09-16
申请号：US17336109
申请日：2021-06-01
Applicant: Adobe Inc.
Inventor： Sanjeev Tagra , Sachin Soni , Ajay Jain , Ryan Rozich , Jonathan Roeder , Prasenjit Mondal
Abstract: Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.
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Patent Agency Ranking
公开(公告)号：US11055905B2
公开(公告)日：2021-07-06
申请号：US16535780
申请日：2019-08-08
Applicant: Adobe Inc.
Inventor： Sanjeev Tagra , Sachin Soni , Ajay Jain , Ryan Rozich , Prasenjit Mondal , Jonathan Roeder
Abstract: Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.
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17.发明申请公开(公告)号：US20200279008A1
公开(公告)日：2020-09-03
申请号：US16288472
申请日：2019-02-28
Applicant: Adobe Inc.
Inventor： Ajay Jain , Sanjeev Tagra , Sachin Soni , Ryan Rozich , Jonathan Roeder
IPC: G06F16/9538 , G06F16/954 , G06F3/01 , G06N20/00
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating target products for a product search based on gesture input received via a digital canvas. For example, the disclosed systems can utilize digital image classification models to generate product sets based on individual visual product features of digital images of products. The disclosed systems can further receive gesture input within a digital canvas indicating visual product features. In addition, the disclosed systems can compare the gesture input of the digital canvas with representative digital images of product sets generated by particular classification models to identify product sets that include the indicated visual product features. Further, the disclosed systems can provide target products from the identified product sets for display via a product search interface website.
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公开(公告)号：US11907280B2
公开(公告)日：2024-02-20
申请号：US17090150
申请日：2020-11-05
Applicant: ADOBE INC.
Inventor： Mikhail Kotov , Roland Geisler , Saeid Motiian , Dylan Nathaniel Warnock , Michele Saad , Venkata Naveen Kumar Yadav Marri , Ajinkya Kale , Ryan Rozich , Baldo Faieta
IPC: G06F17/00 , G06F7/00 , G06F16/532 , G06F16/2457 , G06F16/538 , G06F16/583
CPC classification number: G06F16/532 , G06F16/24578 , G06F16/538 , G06F16/5846
Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.
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公开(公告)号：US20240054991A1
公开(公告)日：2024-02-15
申请号：US17887959
申请日：2022-08-15
Applicant: Adobe Inc.
Inventor： Ajay Jain , Sanjeev Tagra , Sachin Soni , Ryan Rozich , Nikaash Puri , Jonathan Roeder
IPC: G10L15/06 , G06V10/774 , G10L15/183 , G06F40/284 , G06F40/30 , G06F3/16 , G10L15/22 , G06F16/532
CPC classification number: G10L15/063 , G06V10/7747 , G10L15/183 , G06F40/284 , G06F40/30 , G06F3/167 , G10L15/22 , G06F16/532
Abstract: An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.
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公开(公告)号：US20220138247A1
公开(公告)日：2022-05-05
申请号：US17090150
申请日：2020-11-05
Applicant: ADOBE INC.
Inventor： Mikhail Kotov , Roland Geisler , Saeid Motiian , Dylan Nathaniel Warnock , Michele Saad , Venkata Naveen Kumar Yadav Marri , Ajinkya Kale , Ryan Rozich , Baldo Faieta
IPC: G06F16/532 , G06F16/583 , G06F16/538 , G06F16/2457
Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.
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