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公开(公告)号：US11978216B2
公开(公告)日：2024-05-07
申请号：US17696377
申请日：2022-03-16
Applicant: ADOBE INC.
Inventor： Ning Xu
CPC classification number: G06T7/194 , G06N3/04 , G06N3/08 , G06T5/50 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221
Abstract: Methods and systems are provided for generating mattes for input images. A neural network system is trained to generate a matte for an input image utilizing contextual information within the image. Patches from the image and a corresponding trimap are extracted, and alpha values for each individual image patch are predicted based on correlations of features in different regions within the image patch. Predicting alpha values for an image patch may also be based on contextual information from other patches extracted from the same image. This contextual information may be determined by determining correlations between features in the query patch and context patches. The predicted alpha values for an image patch form a matte patch, and all matte patches generated for the patches are stitched together to form an overall matte for the input image.
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公开(公告)号：US20230237709A1
公开(公告)日：2023-07-27
申请号：US17649101
申请日：2022-01-27
Applicant: ADOBE INC.
Inventor： Ning Xu
CPC classification number: G06T11/00 , G06T5/005 , G06T7/11 , G06V10/267 , G06V10/82 , G06T5/50 , G06T2210/22 , G06T2207/20081 , G06T2207/10016 , G06T2207/20132 , G06T2207/20221
Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure identify a first image depicting a first object; identify a plurality of candidate images depicting a second object; select a second image from the plurality of candidate images depicting the second object based on the second image and a sequence of previous images including the first image using a crop selection network trained to select a next compatible image based on the sequence of previous images; and generate a composite image depicting the first object and the second object based on the first image and the second image.
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公开(公告)号：US20230118401A1
公开(公告)日：2023-04-20
申请号：US17502447
申请日：2021-10-15
Applicant: Adobe Inc.
Inventor： Ning Xu
Abstract: Certain aspects and features of this disclosure relate to graph-based video instance segmentation. In one example, a reference instance of an object in a reference frame and features in a target frame are identified and used to produce a graph of nodes and edges. Each node represents a feature in the target frame or the reference instance of the object in the reference frame. Each edge of the graph represents a spatiotemporal relationship between the feature in the target frame and the reference instance of the object. Embeddings of the nodes and edges of the graph are iteratively updated based on the spatiotemporal relationship between a feature in the target frame and the reference instance of the object in the reference frame, resulting in a fused node embedding that can be used for detecting the target instance of the object.
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4.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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公开(公告)号：US20230042221A1
公开(公告)日：2023-02-09
申请号：US17384109
申请日：2021-07-23
Applicant: Adobe Inc.
Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that perform language guided digital image editing utilizing a cycle-augmentation generative-adversarial neural network (CAGAN) that is augmented using a cross-modal cyclic mechanism. For example, the disclosed systems generate an editing description network that generates language embeddings which represent image transformations applied between a digital image and a modified digital image. The disclosed systems can further train a GAN to generate modified images by providing an input image and natural language embeddings generated by the editing description network (representing various modifications to the digital image from a ground truth modified image). In some instances, the disclosed systems also utilize an image request attention approach with the GAN to generate images that include adaptive edits in different spatial locations of the image.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11570318B2
公开(公告)日：2023-01-31
申请号：US17374103
申请日：2021-07-13
Applicant: Adobe Inc.
Inventor： Ning Xu , Jing Shi , Franck Dernoncourt , Trung Bui
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize a neural network having a long short-term memory encoder-decoder architecture to progressively modify a digital image in accordance with a natural language request. For example, in one or more embodiments, the disclosed systems utilize a language-to-operation decoding cell of a language-to-operation neural network to sequentially determine one or more image-modification operations to perform to modify a digital image in accordance with a natural language request. In some cases, the decoding cell determines an image-modification operation to perform partly based on the previously used image-modification operations. The disclosed systems further utilize the decoding cell to determine one or more operation parameters for each selected image-modification operation. The disclosed systems utilize the image-modification operation(s) and operation parameter(s) to modify the digital image (e.g., by generating one or more modified digital images) via the decoding cell.
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公开(公告)号：US11263750B1
公开(公告)日：2022-03-01
申请号：US17086012
申请日：2020-10-30
Applicant: Adobe Inc.
Inventor： Ning Xu
Abstract: Introduced here are computer programs and associated computer-implemented techniques for training and then applying computer-implemented models designed for segmentation of an object in the frames of video. By training and then applying the segmentation model in a cyclical manner, the errors encountered when performing segmentation can be eliminated rather than propagated. In particular, the approach to segmentation described herein allows the relationship between a reference mask and each target frame for which a mask is to be produced to be explicitly bridged or established. Such an approach ensures that masks are accurate, which in turn means that the segmentation model is less prone to distractions.
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公开(公告)号：US11244207B2
公开(公告)日：2022-02-08
申请号：US17101778
申请日：2020-11-23
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Tianlang Chen , Ning Xu , Hailin Jin
IPC: G06K9/00 , G06K9/68 , G06K9/62 , G06K9/46 , G06F16/906 , G06N3/08 , G06F16/903 , G06F40/109
Abstract: The present disclosure relates to a tag-based font recognition system that utilizes a multi-learning framework to develop and improve tag-based font recognition using deep learning neural networks. In particular, the tag-based font recognition system jointly trains a font tag recognition neural network with an implicit font classification attention model to generate font tag probability vectors that are enhanced by implicit font classification information. Indeed, the font recognition system weights the hidden layers of the font tag recognition neural network with implicit font information to improve the accuracy and predictability of the font tag recognition neural network, which results in improved retrieval of fonts in response to a font tag query. Accordingly, using the enhanced tag probability vectors, the tag-based font recognition system can accurately identify and recommend one or more fonts in response to a font tag query.
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公开(公告)号：US20210319564A1
公开(公告)日：2021-10-14
申请号：US16847819
申请日：2020-04-14
Applicant: ADOBE INC.
Inventor： Ning Xu
Abstract: Methods and systems are provided for generating mattes for input images. A neural network system is trained to generate a matte for an input image utilizing contextual information within the image. Patches from the image and a corresponding trimap are extracted, and alpha values for each individual image patch are predicted based on correlations of features in different regions within the image patch. Predicting alpha values for an image patch may also be based on contextual information from other patches extracted from the same image. This contextual information may be determined by determining correlations between features in the query patch and context patches. The predicted alpha values for an image patch form a matte patch, and all matte patches generated for the patches are stitched together to form an overall matte for the input image.
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公开(公告)号：US20210256708A1
公开(公告)日：2021-08-19
申请号：US17313158
申请日：2021-05-06
Applicant: Adobe Inc.
Inventor： Brian Lynn Price , Scott Cohen , Marco Forte , Ning Xu
Abstract: Techniques are disclosed for deep neural network (DNN) based interactive image matting. A methodology implementing the techniques according to an embodiment includes generating, by the DNN, an alpha matte associated with an image, based on user-specified foreground region locations in the image. The method further includes applying a first DNN subnetwork to the image, the first subnetwork trained to generate a binary mask based on the user input, the binary mask designating pixels of the image as background or foreground. The method further includes applying a second DNN subnetwork to the generated binary mask, the second subnetwork trained to generate a trimap based on the user input, the trimap designating pixels of the image as background, foreground, or uncertain status. The method further includes applying a third DNN subnetwork to the generated trimap, the third subnetwork trained to generate the alpha matte based on the user input.
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