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公开(公告)号：US10789456B2
公开(公告)日：2020-09-29
申请号：US15856271
申请日：2017-12-28
Applicant: Adobe Inc.
Inventor： Yu Luo , Xin Lu , Jen-Chan Jeff Chien
Abstract: Techniques are disclosed for a facial expression classification. In an embodiment, a multi-class classifier is trained using labelled training images, each training image including a facial expression. The trained classifier is then used to predict expressions for unlabelled video frames, whereby each frame is effectively labelled with a predicted expression. In addition, each predicted expression can be associated with a confidence score. Anchor frames can then be identified in the labelled video frames, based on the confidence scores of those frames (anchor frames are frames having a confidence score above an established threshold). Then, for each labelled video frame between two anchor frames, the predicted expression is refined or otherwise updated using interpolation, thereby providing a set of video frames having calibrated expression labels. These calibrated labelled video frames can then be used to further train the previously trained facial expression classifier, thereby providing a supplementally trained facial expression classifier.
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公开(公告)号：US10755391B2
公开(公告)日：2020-08-25
申请号：US15980691
申请日：2018-05-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
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公开(公告)号：US20190355102A1
公开(公告)日：2019-11-21
申请号：US15980691
申请日：2018-05-15
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xin Lu , Xiaohui Shen , Jimei Yang , Jiahui Yu
Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.
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公开(公告)号：US20190156122A1
公开(公告)日：2019-05-23
申请号：US15816704
申请日：2017-11-17
Applicant: Adobe Inc.
Inventor： Xin Lu
Abstract: The present disclosure includes systems, methods, and computer readable media, that identify one or more scene categories that correspond to digital images. In one or more embodiments, disclosed systems analyze a digital image to determine, for each of a plurality of object tags, a probability that the object tag associates with the digital image. The systems further determine, for each of the plurality of object tags, a similarity score for each of a plurality of scene categories (e.g., a similarity between each object tag and each scene category). Using the object tag probabilities and the similarity scores, the disclosed systems determine a probability, for each scene category, that the digital image pertains to the scene category. Based on the determined probabilities, the disclosed systems are able to identify an appropriate scene category for the digital image.
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公开(公告)号：US20190130229A1
公开(公告)日：2019-05-02
申请号：US15799395
申请日：2017-10-31
Applicant: Adobe Inc.
Inventor： Xin Lu , Zhe Lin , Xiaohui Shen , Jimei Yang , Jianming Zhang , Jen-Chan Jeff Chien , Chenxi Liu
CPC classification number: G06K9/66 , G06K9/4628 , G06K9/4671 , G06N3/0454 , G06N3/08 , G06T7/194 , G06T2207/20081 , G06T2207/20084
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or more salient content neural networks. In particular, in one or more embodiments, the disclosed systems and methods train one or more salient content neural networks to efficiently identify foreground pixels in digital visual media. Moreover, in one or more embodiments, the disclosed systems and methods provide a trained salient content neural network to a mobile device, allowing the mobile device to directly select salient objects in digital visual media utilizing a trained neural network. Furthermore, in one or more embodiments, the disclosed systems and methods train and provide multiple salient content neural networks, such that mobile devices can identify objects in real-time digital visual media feeds (utilizing a first salient content neural network) and identify objects in static digital images (utilizing a second salient content neural network).
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Patent Agency Ranking
公开(公告)号：US20240346234A1
公开(公告)日：2024-10-17
申请号：US18300721
申请日：2023-04-14
Applicant: ADOBE INC.
Inventor： Xinyang Zhang , Wentian Zhao , Xin Lu , Jen-Chan Chien
IPC: G06F40/166 , G06F40/103 , G06V30/41
CPC classification number: G06F40/166 , G06F40/103 , G06V30/41
Abstract: Systems and methods for document processing are provided. One aspect of the systems and methods includes obtaining a prompt including a document description describing a plurality of elements. A plurality of image assets are generated based on the prompt using a generative neural network. In some cases, the plurality of image assets correspond to the plurality of elements of the document description. A structured document is then generated that matches the document description. In some cases the structured document includes the plurality of image assets and metadata describing a relationship between the plurality of image assets.
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17.发明公开公开(公告)号：US20240161364A1
公开(公告)日：2024-05-16
申请号：US18053646
申请日：2022-11-08
Applicant: Adobe Inc.
Inventor： Zichuan Liu , Xin Lu , Ke Wang
CPC classification number: G06T11/60 , G06T7/13 , G06V10/44 , G06T2207/20084
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.
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公开(公告)号：US11335004B2
公开(公告)日：2022-05-17
申请号：US16988408
申请日：2020-08-07
Applicant: Adobe Inc.
Inventor： Zichuan Liu , Wentian Zhao , Shitong Wang , He Qin , Yumin Jia , Yeojin Kim , Xin Lu , Jen-Chan Chien
IPC: G06T7/11
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.
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公开(公告)号：US20220124257A1
公开(公告)日：2022-04-21
申请号：US17073697
申请日：2020-10-19
Applicant: Adobe Inc.
Inventor： Wentian Zhao , Kun Wan , Xin Lu , Jen-Chan Jeff Chien
Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating artistic images by applying an artistic-effect to one or more frames of a video stream or digital images. In one or more embodiments, the disclosed system captures a video stream utilizing a camera of a computing device. The disclosed system deploys a distilled artistic-effect neural network on the computing device to generate an artistic version of the captured video stream at a first resolution in real time. The disclosed system can provide the artistic video stream for display via the computing device. Based on an indication of a capture event, the disclosed system utilizes the distilled artistic-effect neural network to generate an artistic image at a higher resolution than the artistic video stream. Furthermore, the disclosed system tunes and utilizes an artistic-effect patch generative adversarial neural network to modify parameters for the distilled artistic-effect neural network.
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公开(公告)号：US20220121841A1
公开(公告)日：2022-04-21
申请号：US17075207
申请日：2020-10-20
Applicant: Adobe Inc.
Inventor： Jinoh Oh , Xin Lu , Gahye Park , Jen-Chan Jeff Chien , Yumin Jia
Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.
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