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公开(公告)号：US09965717B2
公开(公告)日：2018-05-08
申请号：US14940916
申请日：2015-11-13
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Zhaowen Wang , Xianming Liu , Hailin Jin , Chen Fang
CPC classification number: G06N3/0454 , G06K9/4628 , G06K9/6257 , G06K9/627 , G06K9/628 , G06K9/6284 , G06K9/629 , G06N3/04 , G06N3/08 , G06Q50/01
Abstract: Embodiments of the present invention relate to learning image representation by distilling from multi-task networks. In implementation, more than one single-task network is trained with heterogeneous labels. In some embodiments, each of the single-task networks is transformed into a Siamese structure with three branches of sub-networks so that a common triplet ranking loss can be applied to each branch. A distilling network is trained that approximates the single-task networks on a common ranking task. In some embodiments, the distilling network is a Siamese network whose ranking function is optimized to approximate an ensemble ranking of each of the single-task networks. The distilling network can be utilized to predict tags to associate with a test image or identify similar images to the test image.
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公开(公告)号：US20170206465A1
公开(公告)日：2017-07-20
申请号：US14996959
申请日：2016-01-15
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhou Ren , Zhe Lin , Chen Fang
CPC classification number: G06N20/00 , G06F16/5866 , G06F17/241 , G06F17/2785 , G06N3/0454 , G06N3/08
Abstract: Modeling semantic concepts in an embedding space as distributions is described. In the embedding space, both images and text labels are represented. The text labels describe semantic concepts that are exhibited in image content. In the embedding space, the semantic concepts described by the text labels are modeled as distributions. By using distributions, each semantic concept is modeled as a continuous cluster which can overlap other clusters that model other semantic concepts. For example, a distribution for the semantic concept “apple” can overlap distributions for the semantic concepts “fruit” and “tree” since can refer to both a fruit and a tree. In contrast to using distributions, conventionally configured visual-semantic embedding spaces represent a semantic concept as a single point. Thus, unlike these conventionally configured embedding spaces, the embedding spaces described herein are generated to model semantic concepts as distributions, such as Gaussian distributions, Gaussian mixtures, and so on.
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公开(公告)号：US09406110B2
公开(公告)日：2016-08-02
申请号：US14968075
申请日：2015-12-14
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Chen Fang
CPC classification number: G06T5/003 , G06F17/30256 , G06F17/3053 , G06T7/00 , G06T11/60 , G06T2207/10004 , G06T2207/10016 , G06T2207/20016 , G06T2207/20092 , G06T2207/20132 , G06T2207/30168 , G06T2210/22
Abstract: Cropping boundary simplicity techniques are described. In one or more implementations, multiple candidate cropping s of a scene are generated. For each of the candidate croppings, a score is calculated that is indicative of a boundary simplicity for the candidate cropping. To calculate the boundary simplicity, complexity of the scene along a boundary of a respective candidate cropping is measured. The complexity is measured, for instance, using an average gradient, an image edge map, or entropy along the boundary. Values indicative of the complexity may be derived from the measuring. The candidate croppings may then be ranked according to those values. Based on the scores calculated to indicate the boundary simplicity, one or more of the candidate croppings may be chosen e.g., to present the chosen croppings to a user for selection.
Abstract translation: 描述边界简单技术。 在一个或多个实现中，生成场景的多个候选裁剪。 对于每个候选作物，计算表示候选种植的边界简单性的分数。 为了计算边界简单性，测量沿着相应候选剪切的边界的场景的复杂性。 测量复杂度，例如，使用沿着边界的平均梯度，图像边缘图或熵。 表示复杂性的值可以从测量得出。 然后可以根据这些值对候选作物进行排序。 基于计算的用于指示边界简单性的分数，可以选择一个或多个候选剪切，以将所选择的剪切呈现给用户进行选择。
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公开(公告)号：US09213919B2
公开(公告)日：2015-12-15
申请号：US14180305
申请日：2014-02-13
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Chen Fang
CPC classification number: G06K9/6212 , G06K9/00664 , G06K9/4642 , G06K2009/6213
Abstract: In techniques for category histogram image representation, image segments of an input image are generated and bounding boxes are selected that each represent a region of the input image, where each of the bounding boxes include image segments of the input image. A saliency map of the input image can also be generated. A bounding box is applied as a query on an images database to determine database image regions that match the region of the input image represented by the bounding box. The query can be augmented based on saliency detection of the input image region that is represented by the bounding box, and a query result is a ranked list of the database image regions. A category histogram for the region of the input image is then generated based on category labels of each of the database image regions that match the input image region.
Abstract translation: 在类别直方图图像表示的技术中，生成输入图像的图像片段，并且选择每个表示输入图像的区域的边界框，其中每个边界框包括输入图像的图像片段。 也可以生成输入图像的显着图。 将边框应用于图像数据库上的查询，以确定与由边界框表示的输入图像的区域匹配的数据库图像区域。 可以基于由边界框表示的输入图像区域的显着性检测来增加查询，并且查询结果是数据库图像区域的排序列表。 然后基于与输入图像区域匹配的每个数据库图像区域的类别标签来生成输入图像的区域的类别直方图。
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公开(公告)号：US20150213612A1
公开(公告)日：2015-07-30
申请号：US14169025
申请日：2014-01-30
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Chen Fang
IPC: G06T7/00
CPC classification number: G06T5/003 , G06F17/30256 , G06F17/3053 , G06T7/00 , G06T11/60 , G06T2207/10004 , G06T2207/10016 , G06T2207/20016 , G06T2207/20092 , G06T2207/20132 , G06T2207/30168 , G06T2210/22
Abstract: Cropping boundary simplicity techniques are described. In one or more implementations, multiple candidate croppings of a scene are generated. For each of the candidate croppings, a score is calculated that is indicative of a boundary simplicity for the candidate cropping. To calculate the boundary simplicity, complexity of the scene along a boundary of a respective candidate cropping is measured. The complexity is measured, for instance, using an average gradient, an image edge map, or entropy along the boundary. Values indicative of the complexity may be derived from the measuring. The candidate croppings may then be ranked according to those values. Based on the scores calculated to indicate the boundary simplicity, one or more of the candidate croppings may be chosen e.g., to present the chosen croppings to a user for selection.
Abstract translation: 描述边界简单技术。 在一个或多个实现中，生成场景的多个候选裁剪。 对于每个候选作物，计算表示候选种植的边界简单性的分数。 为了计算边界简单性，测量沿着相应候选剪切的边界的场景的复杂性。 测量复杂度，例如，使用沿着边界的平均梯度，图像边缘图或熵。 表示复杂性的值可以从测量得出。 然后可以根据这些值对候选作物进行排序。 基于计算的用于指示边界简单性的分数，可以选择一个或多个候选剪切，以将所选择的剪切呈现给用户进行选择。
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Patent Agency Ranking
公开(公告)号：US20180240257A1
公开(公告)日：2018-08-23
申请号：US15438147
申请日：2017-02-21
Applicant: Adobe Systems Incorporated
Inventor： Yijun Li , Chen Fang , Jimei Yang , Zhaowen Wang , Xin Lu
CPC classification number: G06T11/60 , G06K9/46 , G06K9/6256 , G06N3/0454 , G06N3/0472 , G06N3/084 , G06T11/001
Abstract: In some embodiments, techniques for synthesizing an image style based on a plurality of neural networks are described. A computer system selects a style image based on user input that identifies the style image. The computer system generates an image based on a generator neural network and a loss neural network. The generator neural network outputs the synthesized image based on a noise vector and the style image and is trained based on style features generated from the loss neural network. The loss neural network outputs the style features based on a training image. The training image and the style image have a same resolution. The style features are generated at different resolutions of the training image. The computer system provides the synthesized image to a user device in response to the user input.
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17.发明申请公开(公告)号：US20180143988A1
公开(公告)日：2018-05-24
申请号：US15357864
申请日：2016-11-21
Applicant: Adobe Systems Incorporated
Inventor： Matthew Douglas Hoffman , Longqi Yang , Hailin Jin , Chen Fang
IPC: G06F17/30
Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.
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18.发明申请公开(公告)号：US20180121768A1
公开(公告)日：2018-05-03
申请号：US15429769
申请日：2017-02-10
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Mai Long , Jonathan Brandt , Hailin Jin , Chen Fang
CPC classification number: G06K9/66 , G06F16/532 , G06K9/4628 , G06K9/52 , G06K9/6215 , G06K9/6256 , G06K9/6267 , G06K9/6271 , G06K9/726 , G06K2009/4666 , G06N3/0454 , G06N3/08
Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.
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公开(公告)号：US20170206435A1
公开(公告)日：2017-07-20
申请号：US14997011
申请日：2016-01-15
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhou Ren , Zhe Lin , Chen Fang
CPC classification number: G06K9/628 , G06F17/30253 , G06F17/30268 , G06K9/00684 , G06K9/66 , G06K9/726
Abstract: Embedding space for images with multiple text labels is described. In the embedding space both text labels and image regions are embedded. The text labels embedded describe semantic concepts that can be exhibited in image content. The embedding space is trained to semantically relate the embedded text labels so that labels like “sun” and “sunset” are more closely related than “sun” and “bird”. Training the embedding space also includes mapping representative images, having image content which exemplifies the semantic concepts, to respective text labels. Unlike conventional techniques that embed an entire training image into the embedding space for each text label associated with the training image, the techniques described herein process a training image to generate regions that correspond to the multiple text labels. The regions of the training image are then embedded into the training space in a manner that maps the regions to the corresponding text labels.
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公开(公告)号：US20170200066A1
公开(公告)日：2017-07-13
申请号：US14995042
申请日：2016-01-13
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Quanzeng You , Hailin Jin , Chen Fang
CPC classification number: G06K9/6269 , G06F17/30253 , G06F17/3028 , G06K9/00664 , G06K9/4604 , G06K9/4628 , G06K9/6202 , G06K9/6274 , G06N3/08
Abstract: Techniques for image captioning with word vector representations are described. In implementations, instead of outputting results of caption analysis directly, the framework is adapted to output points in a semantic word vector space. These word vector representations reflect distance values in the context of the semantic word vector space. In this approach, words are mapped into a vector space and the results of caption analysis are expressed as points in the vector space that capture semantics between words. In the vector space, similar concepts with have small distance values. The word vectors are not tied to particular words or a single dictionary. A post-processing step is employed to map the points to words and convert the word vector representations to captions. Accordingly, conversion is delayed to a later stage in the process.
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