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Patent Agency Ranking
公开(公告)号：US20190286932A1
公开(公告)日：2019-09-19
申请号：US15921492
申请日：2018-03-14
Applicant: Adobe Inc.
Inventor： Delun Du , Zhe Lin , Baldo Faieta
Abstract: The present disclosure is directed toward systems and methods for detecting an object in an input image based on a target object keyword. For example, one or more embodiments described herein generate a heat map of the input image based on the target object keyword and generate various bounding boxes based on a pixel analysis of the heat map. One or more embodiments described herein then utilize the various bounding boxes to determine scores for generated object location proposals in order to provide a highest scoring object location proposal overlaid on the input image.
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公开(公告)号：US20190213474A1
公开(公告)日：2019-07-11
申请号：US15866129
申请日：2018-01-09
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xiaohui Shen , Radomir Mech , Jian Ren
CPC classification number: G06N3/08 , G06F16/784 , G06K9/00221 , G06K9/00744 , G06K9/6256
Abstract: Various embodiments describe frame selection based on training and using a neural network. In an example, the neural network is a convolutional neural network trained with training pairs. Each training pair includes two training frames from a frame collection. The loss function relies on the estimated quality difference between the two training frames. Further, the definition of the loss function varies based on the actual quality difference between these two frames. In a further example, the neural network is trained by incorporating facial heatmaps generated from the training frames and facial quality scores of faces detected in the training frames. In addition, the training involves using a feature mean that represents an average of the features of the training frames belonging to the same frame collection. Once the neural network is trained, a frame collection is input thereto and a frame is selected based on generated quality scores.
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公开(公告)号：US10346996B2
公开(公告)日：2019-07-09
申请号：US14832328
申请日：2015-08-21
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Scott D. Cohen , Brian L. Price
Abstract: Image depth inference techniques and systems from semantic labels are described. In one or more implementations, a digital medium environment includes one or more computing devices to control a determination of depth within an image. Regions of the image are semantically labeled by the one or more computing devices. At least one of the semantically labeled regions is decomposed into a plurality of segments formed as planes generally perpendicular to a ground plane of the image. Depth of one or more of the plurality of segments is then inferred based on relationships of respective segments with respective locations of the ground plane of the image. A depth map is formed that describes depth for the at least one semantically labeled region based at least in part on the inferred depths for the one or more of the plurality of segments.
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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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