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公开(公告)号：US12183056B2
公开(公告)日：2024-12-31
申请号：US17573041
申请日：2022-01-11
Applicant: Adobe Inc.
Inventor： Maksym Andriushchenko , John Collomosse , Xiaoyang Li , Geoffrey Oxholm
IPC: G06V10/75 , G06F16/58 , G06F16/583 , G06N3/084 , G06V10/72 , G06V10/771
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a deep visual fingerprinting model with parameters learned from robust contrastive learning to identify matching digital images and image provenance information. For example, the disclosed systems utilize an efficient learning procedure that leverages training on bounded adversarial examples to more accurately identify digital images (including adversarial images) with a small computational overhead. To illustrate, the disclosed systems utilize a first objective function that iteratively identifies augmentations to increase contrastive loss. Moreover, the disclosed systems utilize a second objective function that iteratively learns parameters of a deep visual fingerprinting model to reduce the contrastive loss. With these learned parameters, the disclosed systems utilize the deep visual fingerprinting model to generate visual fingerprints for digital images, retrieve and match digital images, and provide digital image provenance information.
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2.发明公开公开(公告)号：US20240281504A1
公开(公告)日：2024-08-22
申请号：US18172714
申请日：2023-02-22
Applicant: ADOBE INC.
Inventor： John Collomosse , Andrew S. Parsons
CPC classification number: G06F21/105 , H04L9/3213 , H04L2209/603
Abstract: Systems and methods for managing rights for creative work are provided. One aspect of the systems and methods includes receiving, at a rights contract on a distributed virtual machine operated based on a public blockchain, input data including an ownership token identifier for an ownership token, where the ownership token indicates ownership of a creative work. Another aspect of the systems and methods includes obtaining, at the rights contract, an indication of usage rights for the creative work corresponding to the ownership token. Yet another aspect of the systems and methods includes minting, via the rights contract, a rights token corresponding to the ownership token, where the rights token includes a reference to the indication of the usage rights for the creative work.
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公开(公告)号：US20230386054A1
公开(公告)日：2023-11-30
申请号：US17804376
申请日：2022-05-27
Applicant: Adobe Inc. , University of Surrey
Inventor： John Collomosse , Alexander Black , Van Tu Bui , Hailin Jin , Viswanathan Swaminathan
CPC classification number: G06T7/337 , G06T3/0093 , G06N3/04 , G06T2207/20221 , G06T2207/20084
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to identify regions of an image that have been editorially modified. For example, the image comparison system includes a deep image comparator model that compares a pair of images and localizes regions that have been editorially manipulated relative to an original or trusted image. More specifically, the deep image comparator model generates and surfaces visual indications of the location of such editorial changes on the modified image. The deep image comparator model is robust and ignores discrepancies due to benign image transformations that commonly occur during electronic image distribution. The image comparison system optionally includes an image retrieval model utilizes a visual search embedding that is robust to minor manipulations or benign modifications of images. The image retrieval model utilizes a visual search embedding for an image to robustly identify near duplicate images.
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公开(公告)号：US11823322B2
公开(公告)日：2023-11-21
申请号：US17807337
申请日：2022-06-16
Applicant: Adobe Inc.
Inventor： Tong He , John Collomosse , Hailin Jin
CPC classification number: G06T15/08 , G06T7/74 , G06V10/454 , G06V10/82 , G06V20/647 , G06T2200/08 , G06T2207/20084
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels. Then, the disclosed systems can utilize a patch-based neural rendering approach to render images from frustum feature patches to display a view of the object from various viewpoints.
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公开(公告)号：US12081827B2
公开(公告)日：2024-09-03
申请号：US17822573
申请日：2022-08-26
Applicant: Adobe Inc. , University of Surrey
Inventor： Alexander Black , Van Tu Bui , John Collomosse , Simon Jenni , Viswanathan Swaminathan
IPC: H04N21/434 , G06F16/732 , G06F16/78 , H04N21/84 , H04N21/845
CPC classification number: H04N21/4341 , G06F16/732 , G06F16/7867 , H04N21/84 , H04N21/8456
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.
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Patent Agency Ranking
公开(公告)号：US20240073478A1
公开(公告)日：2024-02-29
申请号：US17822573
申请日：2022-08-26
Applicant: Adobe Inc. , University of Surrey
Inventor： Alexander Black , Van Tu Bui , John Collomosse , Simon Jenni , Viswanathan Swaminathan
IPC: H04N21/434 , G06F16/732 , G06F16/78 , H04N21/84 , H04N21/845
CPC classification number: H04N21/4341 , G06F16/732 , G06F16/7867 , H04N21/84 , H04N21/8456
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize deep learning to map query videos to known videos so as to identify a provenance of the query video or identify editorial manipulations of the query video relative to a known video. For example, the video comparison system includes a deep video comparator model that generates and compares visual and audio descriptors utilizing codewords and an inverse index. The deep video comparator model is robust and ignores discrepancies due to benign transformations that commonly occur during electronic video distribution.
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公开(公告)号：US20220092108A1
公开(公告)日：2022-03-24
申请号：US17025041
申请日：2020-09-18
Applicant: Adobe Inc.
Inventor： John Collomosse , Zhe Lin , Saeid Motiian , Hailin Jin , Baldo Faieta , Alex Filipkowski
IPC: G06F16/583 , G06F16/535 , G06F16/532 , G06N3/08
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.
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公开(公告)号：US11704559B2
公开(公告)日：2023-07-18
申请号：US16904460
申请日：2020-06-17
Applicant: Adobe Inc.
Inventor： John Collomosse
IPC: G06N3/08 , G06N3/04 , G06F18/2135 , G06N3/045 , G06F8/38 , G06F16/583
CPC classification number: G06N3/08 , G06F8/38 , G06F16/583 , G06F18/21355 , G06N3/04 , G06N3/045
Abstract: Embodiments are disclosed for learning structural similarity of user experience (UX) designs using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise generating a representation of a layout of a graphical user interface (GUI), the layout including a plurality of control components, each control component including a control type, geometric features, and relationship features to at least one other control component, generating a search embedding for the representation of the layout using a neural network, and querying a repository of layouts in embedding space using the search embedding to obtain a plurality of layouts based on similarity to the layout of the GUI in the embedding space.
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公开(公告)号：US20220327767A1
公开(公告)日：2022-10-13
申请号：US17807337
申请日：2022-06-16
Applicant: Adobe Inc.
Inventor： Tong He , John Collomosse , Hailin Jin
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for utilizing an encoder-decoder architecture to learn a volumetric 3D representation of an object using digital images of the object from multiple viewpoints to render novel views of the object. For instance, the disclosed systems can utilize patch-based image feature extraction to extract lifted feature representations from images corresponding to different viewpoints of an object. Furthermore, the disclosed systems can model view-dependent transformed feature representations using learned transformation kernels. In addition, the disclosed systems can recurrently and concurrently aggregate the transformed feature representations to generate a 3D voxel representation of the object. Furthermore, the disclosed systems can sample frustum features using the 3D voxel representation and transformation kernels. Then, the disclosed systems can utilize a patch-based neural rendering approach to render images from frustum feature patches to display a view of the object from various viewpoints.
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公开(公告)号：US11709885B2
公开(公告)日：2023-07-25
申请号：US17025041
申请日：2020-09-18
Applicant: Adobe Inc.
Inventor： John Collomosse , Zhe Lin , Saeid Motiian , Hailin Jin , Baldo Faieta , Alex Filipkowski
IPC: G06T7/00 , G06F16/583 , G06F16/532 , G06N3/08 , G06F16/535 , G06V10/82 , G06V20/30
CPC classification number: G06F16/5854 , G06F16/532 , G06F16/535 , G06F16/5838 , G06N3/08 , G06V10/82 , G06V20/30
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly identifying digital images with similar style to a query digital image using fine-grain style determination via weakly supervised style extraction neural networks. For example, the disclosed systems can extract a style embedding from a query digital image using a style extraction neural network such as a novel two-branch autoencoder architecture or a weakly supervised discriminative neural network. The disclosed systems can generate a combined style embedding by combining complementary style embeddings from different style extraction neural networks. Moreover, the disclosed systems can search a repository of digital images to identify digital images with similar style to the query digital image. The disclosed systems can also learn parameters for one or more style extraction neural network through weakly supervised training without a specifically labeled style ontology for sample digital images.
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