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公开(公告)号：US20210295571A1
公开(公告)日：2021-09-23
申请号：US16823092
申请日：2020-03-18
Applicant: Adobe Inc.
Inventor： Xin Sun , Ruben Villegas , Manuel Lagunas Arto , Jimei Yang , Jianming Zhang
Abstract: Introduced here are techniques for relighting an image by automatically segmenting a human object in an image. The segmented image is input to an encoder that transforms it into a feature space. The feature space is concatenated with coefficients of a target illumination for the image and input to an albedo decoder and a light transport detector to predict an albedo map and a light transport matrix, respectively. In addition, the output of the encoder is concatenated with outputs of residual parts of each decoder and fed to a light coefficients block, which predicts coefficients of the illumination for the image. The light transport matrix and predicted illumination coefficients are multiplied to obtain a shading map that can sharpen details of the image. Scaling the resulting image by the albedo map to produce the relight image. The relight image can be refined to denoise the relight image.
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22.发明申请公开(公告)号：US20210271707A1
公开(公告)日：2021-09-02
申请号：US16803480
申请日：2020-02-27
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xihui Liu , Quan Tran , Jianming Zhang , Handong Zhao
IPC: G06F16/583 , G06K9/62 , G06K9/72 , G06F40/30 , G06F16/538 , G06F16/56 , G06F16/2457 , G06N3/08
Abstract: Certain embodiments involve a method for generating a search result. The method includes processing devices performing operations including receiving a query having a text input by a joint embedding model trained to generate an image result. Training the joint embedding model includes accessing a set of images and textual information. Training further includes encoding the images into image feature vectors based on spatial features. Further, training includes encoding the textual information into textual feature vectors based on semantic information. Training further includes generating a set of image-text pairs based on matches between image feature vectors and textual feature vectors. Further, training includes generating a visual grounding dataset based on spatial information. Training further includes generating a set of visual-semantic joint embeddings by grounding the image-text pairs with the visual grounding dataset. Additionally, operations include generating an image result for display by the joint embedding model based on the text input.
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公开(公告)号：US20210256656A1
公开(公告)日：2021-08-19
申请号：US17306249
申请日：2021-05-03
Applicant: ADOBE INC.
Inventor： Jianming Zhang
Abstract: A crop generation system determines multiple types of saliency data and multiple crop candidates for an image. Multiple region of interest (“ROI”) ensembles are generated, indicating locations of the salient content of the image. For each crop candidate, the crop generation system calculates an evaluation score. A set of crop candidates is selected based on the evaluation scores.
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24.发明授权公开(公告)号：US10915798B1
公开(公告)日：2021-02-09
申请号：US15980636
申请日：2018-05-15
Applicant: ADOBE INC.
Inventor： Jianming Zhang , Rameswar Panda , Haoxiang Li , Joon-Young Lee , Xin Lu
Abstract: Disclosed herein are embodiments of systems, methods, and products for a webly supervised training of a convolutional neural network (CNN) to predict emotion in images. A computer may query one or more image repositories using search keywords generated based on the tertiary emotion classes of Parrott's emotion wheel. The computer may filter images received in response to the query to generate a weakly labeled training dataset labels associated with the images that are noisy or wrong may be cleaned prior to training of the CNN. The computer may iteratively train the CNN leveraging the hierarchy of emotion classes by increasing the complexity of the labels (tags) for each iteration. Such curriculum guided training may generate a trained CNN that is more accurate than the conventionally trained neural networks.
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公开(公告)号：US10810707B2
公开(公告)日：2020-10-20
申请号：US16204675
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
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Patent Agency Ranking
公开(公告)号：US20200175651A1
公开(公告)日：2020-06-04
申请号：US16204675
申请日：2018-11-29
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zhe Lin , Xiaohui Shen , Oliver Wang , Lijun Wang
Abstract: Techniques of generating depth-of-field blur effects on digital images by digital effect generation system of a computing device are described. The digital effect generation system is configured to generate depth-of-field blur effects on objects based on focal depth value that defines a depth plane in the digital image and a aperture value that defines an intensity of blur effect applied to the digital image. The digital effect generation system is also configured to improve the accuracy with which depth-of-field blur effects are generated by performing up-sampling operations and implementing a unique focal loss algorithm that minimizes the focal loss within digital images effectively.
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公开(公告)号：US10664719B2
公开(公告)日：2020-05-26
申请号：US15043174
申请日：2016-02-12
Applicant: ADOBE INC.
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang , Chen Fang
IPC: G06K9/62 , G06K9/46 , G06F16/583 , G06N20/10 , G06F16/51 , G06F16/28 , G06F16/2457 , G06N3/04 , G06N3/08
Abstract: Embodiments of the present invention provide an automated image tagging system that can predict a set of tags, along with relevance scores, that can be used for keyword-based image retrieval, image tag proposal, and image tag auto-completion based on user input. Initially, during training, a clustering technique is utilized to reduce cluster imbalance in the data that is input into a convolutional neural network (CNN) for training feature data. In embodiments, the clustering technique can also be utilized to compute data point similarity that can be utilized for tag propagation (to tag untagged images). During testing, a diversity based voting framework is utilized to overcome user tagging biases. In some embodiments, bigram re-weighting can down-weight a keyword that is likely to be part of a bigram based on a predicted tag set.
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公开(公告)号：US10516830B2
公开(公告)日：2019-12-24
申请号：US15730614
申请日：2017-10-11
Applicant: Adobe Inc.
Inventor： Jianming Zhang , Zijun Wei , Zhe Lin , Xiaohui Shen , Radomir Mech
Abstract: Various embodiments describe facilitating real-time crops on an image. In an example, an image processing application executed on a device receives image data corresponding to a field of view of a camera of the device. The image processing application renders a major view on a display of the device in a preview mode. The major view presents a previewed image based on the image data. The image processing application receives a composition score of a cropped image from a deep-learning system. The image processing application renders a sub-view presenting the cropped image based on the composition score in a preview mode. Based on a user interaction, the image processing application renders the cropped image in the major view with the sub-view in the preview mode.
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公开(公告)号：US10346951B2
公开(公告)日：2019-07-09
申请号：US15448138
申请日：2017-03-02
Applicant: Adobe Inc.
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
Abstract: Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, indicative of visual characteristics established for visually-pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which a candidate image cropping is scored may be indicative of its composition quality, an extent to which it preserves content appearing in the scene, and a simplicity of its boundary. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that croppings in a group are different by less than a threshold amount and croppings in different groups are different by at least the threshold amount. Based on the clustering, croppings may then be chosen, e.g., to present them to a user for selection.
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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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