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公开(公告)号：US20190114774A1
公开(公告)日：2019-04-18
申请号：US15784918
申请日：2017-10-16
Applicant: Adobe Systems Incorporated
Inventor： Jianming Zhang
Abstract: A multi-branch neural network generates segmentation data for a received image. The received image is provided to a high-level branch and a low-level branch. Based on the received image, the high-level branch generates a feature map of high-level image features, and the low-level branch generates a feature map of low-level image features. The high-level feature map and the low-level feature map are combined to generate a combined feature map. The combined feature map is provided to a boundary refinement module that includes a dense-connection neural network, which generates segmentation data for the received image, based on the combined feature map.
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公开(公告)号：US20170344848A1
公开(公告)日：2017-11-30
申请号：US15166164
申请日：2016-05-26
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US20160104055A1
公开(公告)日：2016-04-14
申请号：US14511001
申请日：2014-10-09
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang , Anant Gilra , Jen-Chan Jeff Chien
CPC classification number: G06T3/40 , G06K9/4671 , G06T3/0012 , G06T11/60 , G06T2210/22
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.
Abstract translation: 描述了使用多个显着图的图像裁剪建议。 在一个或多个实现中，针对使用多个不同显着图的候选图像裁剪计算指示为视觉上令人满意的裁剪而建立的视觉特征的分数分数。 评估候选图像裁剪的视觉特征可以指示其组成质量，其保存出现在场景中的内容的程度以及其边界的简单性。 基于分量分数，可以根据每个视觉特征来排列裁剪。 排名可以用于将候选作物聚类成类似的作物的组，使得组中的作物差异小于阈值量，并且不同组中的剪切至少达到阈值量。 基于聚类，可以选择裁剪，例如将其呈现给用户进行选择。
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公开(公告)号：US20190109981A1
公开(公告)日：2019-04-11
申请号：US15730614
申请日：2017-10-11
Applicant: Adobe Systems Incorporated
Inventor： Jianming Zhang , Zijun Wei , Zhe Lin , Xiaohui Shen , Radomir Mech
IPC: H04N5/232
CPC classification number: H04N5/232935 , G06N3/08 , H04N5/23222 , H04N5/23229 , H04N5/23293
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：US10235623B2
公开(公告)日：2019-03-19
申请号：US15094633
申请日：2016-04-08
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang , Chen Fang
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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Patent Agency Ranking
公开(公告)号：US09990558B2
公开(公告)日：2018-06-05
申请号：US15705151
申请日：2017-09-14
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US20180005070A1
公开(公告)日：2018-01-04
申请号：US15705151
申请日：2017-09-14
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US09846840B1
公开(公告)日：2017-12-19
申请号：US15164310
申请日：2016-05-25
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan W. Brandt , Jianming Zhang
CPC classification number: G06N3/084 , G06F17/30259
Abstract: Semantic class localization techniques and systems are described. In one or more implementation, a technique is employed to back communicate relevancies of aggregations back through layers of a neural network. Through use of these relevancies, activation relevancy maps are created that describe relevancy of portions of the image to the classification of the image as corresponding to a semantic class. In this way, the semantic class is localized to portions of the image. This may be performed through communication of positive and not negative relevancies, use of contrastive attention maps to different between semantic classes and even within a same semantic class through use of a self-contrastive technique.
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公开(公告)号：US09830526B1
公开(公告)日：2017-11-28
申请号：US15166164
申请日：2016-05-26
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Xiaohui Shen , Jonathan Brandt , Jianming Zhang
CPC classification number: G06K9/4628 , G06K9/6298 , G06K2009/00322 , G06N3/04 , G06N3/08
Abstract: Techniques for increasing robustness of a convolutional neural network based on training that uses multiple datasets and multiple tasks are described. For example, a computer system trains the convolutional neural network across multiple datasets and multiple tasks. The convolutional neural network is configured for learning features from images and accordingly generating feature vectors. By using multiple datasets and multiple tasks, the robustness of the convolutional neural network is increased. A feature vector of an image is used to apply an image-related operation to the image. For example, the image is classified, indexed, or objects in the image are tagged based on the feature vector. Because the robustness is increased, the accuracy of the generating feature vectors is also increased. Hence, the overall quality of an image service is enhanced, where the image service relies on the image-related operation.
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公开(公告)号：US09805445B2
公开(公告)日：2017-10-31
申请号：US14524489
申请日：2014-10-27
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Brian L. Price , Jianming Zhang
IPC: G06T3/40
CPC classification number: G06T3/40
Abstract: Image zooming is described. In one or more implementations, zoomed croppings of an image are scored. The scores calculated for the zoomed croppings are indicative of a zoomed cropping's inclusion of content that is captured in the image. For example, the scores are indicative of a degree to which a zoomed cropping includes salient content of the image, a degree to which the salient content included in the zoomed cropping is centered in the image, and a degree to which the zoomed cropping preserves specified regions-to-keep and excludes specified regions-to-remove. Based on the scores, at least one zoomed cropping may be chosen to effectuate a zooming of the image. Accordingly, the image may be zoomed according to the zoomed cropping such that an amount the image is zoomed corresponds to a scale of the zoomed cropping.
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