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公开(公告)号：US12260557B2
公开(公告)日：2025-03-25
申请号：US17838995
申请日：2022-06-13
Applicant: ADOBE INC.
Inventor： Zijun Wei , Yilin Wang , Jianming Zhang , He Zhang
IPC: G06K9/00 , G06T7/11 , G06T7/136 , G06V10/46 , G06V10/764
Abstract: An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.
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公开(公告)号：US20230401716A1
公开(公告)日：2023-12-14
申请号：US17806312
申请日：2022-06-10
Applicant: ADOBE INC.
Inventor： Yilin Wang , Chenglin Yang , Jianming Zhang , He Zhang , Zijun Wei , Zhe Lin
Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing a convolutional self-attention operation that outputs a plurality of attention-weighted values for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.
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公开(公告)号：US20230135978A1
公开(公告)日：2023-05-04
申请号：US17513559
申请日：2021-10-28
Applicant: Adobe Inc.
Inventor： Brian Price , Yutong Dai , He Zhang
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a transformer-based encoder-decoder neural network architecture for generating alpha mattes for digital images. Specifically, the disclosed system utilizes a transformer encoder to generate patch-based encodings from a digital image and a trimap segmentation by generating patch encodings for image patches and comparing the patch encodings to areas of the digital image. Additionally, the disclosed system generates modified patch-based encodings utilizing a plurality of neural network layers. The disclosed system also generates an alpha matte for the digital image from the patch-based encodings utilizing a decoder that includes a plurality of upsampling layers connected to a plurality of neural network layers via skip connections. In additional embodiments, the disclosed system generates the alpha matte based on additional encodings generated by a plurality of convolutional neural network layers connected to a subset of the upsampling layers via skip connections.
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公开(公告)号：US12165292B2
公开(公告)日：2024-12-10
申请号：US18317547
申请日：2023-05-15
Applicant: Adobe Inc.
Inventor： He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
IPC: G06T5/75 , G06N3/08 , G06T3/4046 , G06T7/194
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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公开(公告)号：US12165284B2
公开(公告)日：2024-12-10
申请号：US17655663
申请日：2022-03-21
Applicant: Adobe Inc.
Inventor： He Zhang , Jianming Zhang , Jose Ignacio Echevarria Vallespi , Kalyan Sunkavalli , Meredith Payne Stotzner , Yinglan Ma , Zhe Lin , Elya Shechtman , Frederick Mandia
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a dual-branched neural network architecture to harmonize composite images. For example, in one or more implementations, the transformer-based harmonization system uses a convolutional branch and a transformer branch to generate a harmonized composite image based on an input composite image and a corresponding segmentation mask. More particularly, the convolutional branch comprises a series of convolutional neural network layers followed by a style normalization layer to extract localized information from the input composite image. Further, the transformer branch comprises a series of transformer neural network layers to extract global information based on different resolutions of the input composite image. Utilizing a decoder, the transformer-based harmonization system combines the local information and the global information from the corresponding convolutional branch and transformer branch to generate a harmonized composite image.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20230281763A1
公开(公告)日：2023-09-07
申请号：US18317547
申请日：2023-05-15
Applicant: Adobe Inc.
Inventor： He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
CPC classification number: G06T5/004 , G06T7/194 , G06N3/08 , G06T3/4046 , G06T2207/20132 , G06T2207/20084 , G06T2207/20081
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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公开(公告)号：US11651477B2
公开(公告)日：2023-05-16
申请号：US16988055
申请日：2020-08-07
Applicant: Adobe Inc.
Inventor： He Zhang , Seyed Morteza Safdarnejad , Yilin Wang , Zijun Wei , Jianming Zhang , Salil Tambe , Brian Price
CPC classification number: G06T5/004 , G06N3/08 , G06T3/4046 , G06T7/194 , G06T2207/20081 , G06T2207/20084 , G06T2207/20132
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing a plurality of neural networks in a multi-branch pipeline to generate image masks for digital images. Specifically, the disclosed system can classify a digital image as a portrait or a non-portrait image. Based on classifying a portrait image, the disclosed system can utilize separate neural networks to generate a first mask portion for a portion of the digital image including a defined boundary region and a second mask portion for a portion of the digital image including a blended boundary region. The disclosed system can generate the mask portion for the blended boundary region by utilizing a trimap generation neural network to automatically generate a trimap segmentation including the blended boundary region. The disclosed system can then merge the first mask portion and the second mask portion to generate an image mask for the digital image.
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公开(公告)号：US20230112186A1
公开(公告)日：2023-04-13
申请号：US17500736
申请日：2021-10-13
Applicant: Adobe Inc.
Inventor： Brian Price , Yutong Dai , He Zhang
Abstract: This disclosure describes one or more implementations of an alpha matting system that utilizes a deep learning model to generate alpha mattes for digital images utilizing an alpha-range classifier function. More specifically, in various implementations, the alpha matting system builds and utilizes an object mask neural network having a decoder that includes an alpha-range classifier to determine classification probabilities for pixels of a digital image with respect to multiple alpha-range classifications. In addition, the alpha matting system can utilize a refinement model to generate the alpha matte from the pixel classification probabilities with respect to the multiple alpha-range classifications.
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9.发明授权公开(公告)号：US11593948B2
公开(公告)日：2023-02-28
申请号：US17177595
申请日：2021-02-17
Applicant: Adobe Inc.
Inventor： Qihang Yu , Jianming Zhang , He Zhang , Yilin Wang , Zhe Lin , Ning Xu
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.
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10.发明授权公开(公告)号：US12299844B2
公开(公告)日：2025-05-13
申请号：US18440248
申请日：2024-02-13
Applicant: Adobe Inc.
Inventor： He Zhang , Yifan Jiang , Yilin Wang , Jianming Zhang , Kalyan Sunkavalli , Sarah Kong , Su Chen , Sohrab Amirghodsi , Zhe Lin
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.
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