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公开(公告)号：US09792534B2
公开(公告)日：2017-10-17
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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公开(公告)号：US20170228659A1
公开(公告)日：2017-08-10
申请号：US15082877
申请日：2016-03-28
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jianchao Yang , Hailin Jin , Chen Fang
CPC classification number: G06N20/00 , G06F16/532 , G06F16/58 , G06N3/0454 , G06N3/08 , G06N5/04
Abstract: Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.
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公开(公告)号：US20170200065A1
公开(公告)日：2017-07-13
申请号：US14995032
申请日：2016-01-13
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Quanzeng You , Hailin Jin , Chen Fang
CPC classification number: G06K9/6269 , G06F17/30247 , G06F17/3028 , G06F17/30675 , G06K9/00664 , G06K9/4604 , G06K9/4628 , G06K9/6202 , G06K9/6274 , G06N3/0445 , G06N3/08 , G06N7/005
Abstract: Techniques for image captioning with weak supervision are described herein. In implementations, weak supervision data regarding a target image is obtained and utilized to provide detail information that supplements global image concepts derived for image captioning. Weak supervision data refers to noisy data that is not closely curated and may include errors. Given a target image, weak supervision data for visually similar images may be collected from sources of weakly annotated images, such as online social networks. Generally, images posted online include “weak” annotations in the form of tags, titles, labels, and short descriptions added by users. Weak supervision data for the target image is generated by extracting keywords for visually similar images discovered in the different sources. The keywords included in the weak supervision data are then employed to modulate weights applied for probabilistic classifications during image captioning analysis.
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公开(公告)号：US09251594B2
公开(公告)日：2016-02-02
申请号：US14169025
申请日：2014-01-30
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 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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公开(公告)号：US09245347B2
公开(公告)日：2016-01-26
申请号：US14169073
申请日：2014-01-30
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Radomir Mech , Xiaohui Shen , Chen Fang
CPC classification number: G06T7/0081 , G06F3/04845 , G06T7/00 , G06T11/60 , G06T2207/10004 , G06T2207/20016 , G06T2207/20081 , G06T2207/20132 , G06T2207/30168
Abstract: Image cropping suggestion is described. In one or more implementations, multiple croppings of a scene are scored based on parameters that indicate visual characteristics established for visually pleasing croppings. The parameters may include a parameter that indicates composition quality of a candidate cropping, for example. The parameters may also include a parameter that indicates whether content appearing in the scene is preserved and a parameter that indicates simplicity of a boundary of a candidate cropping. Based on the scores, image croppings may be chosen, e.g., to present the chosen image croppings to a user for selection. To choose the croppings, they may be ranked according to the score and chosen such that consecutively ranked croppings are not chosen. Alternately or in addition, image croppings may be chosen that are visually different according to scores which indicate those croppings have different visual characteristics.
Abstract translation: 描述了图像裁剪建议。 在一个或多个实现中，基于指示为视觉上令人满意的裁剪而建立的视觉特征的参数对场景进行多次裁剪。 参数可以包括例如表示候选裁剪的组合质量的参数。 参数还可以包括指示是否保存出现在场景中的内容的参数以及指示候选裁剪边界的简单性的参数。 基于分数，可以选择图像裁切，例如，将所选择的图像裁切呈现给用户进行选择。 要选择裁剪，可以根据分数进行排序，并选择不选择连续排序的裁剪。 或者或另外，可以根据指示这些裁剪具有不同视觉特征的分数在视觉上不同地选择图像裁切。
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Patent Agency Ranking
公开(公告)号：US20150227817A1
公开(公告)日：2015-08-13
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：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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公开(公告)号：US20160098823A1
公开(公告)日：2016-04-07
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：US20180373979A1
公开(公告)日：2018-12-27
申请号：US15630604
申请日：2017-06-22
Applicant: Adobe Systems Incorporated
Inventor： Zhaowen Wang , Shuai Tang , Hailin Jin , Chen Fang
Abstract: The present disclosure includes methods and systems for generating captions for digital images. In particular, the disclosed systems and methods can train an image encoder neural network and a sentence decoder neural network to generate a caption from an input digital image. For instance, in one or more embodiments, the disclosed systems and methods train an image encoder neural network (e.g., a character-level convolutional neural network) utilizing a semantic similarity constraint, training images, and training captions. Moreover, the disclosed systems and methods can train a sentence decoder neural network (e.g., a character-level recurrent neural network) utilizing training sentences and an adversarial classifier.
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公开(公告)号：US20180150947A1
公开(公告)日：2018-05-31
申请号：US15457830
申请日：2017-03-13
Applicant: ADOBE SYSTEMS INCORPORATED
Inventor： Jingwan Lu , Patsorn Sangkloy , Chen Fang
CPC classification number: G06T5/20 , G06N3/0454 , G06N3/08 , G06T11/00 , G06T11/001 , G06T2207/20081 , G06T2207/20084
Abstract: Methods and systems are provided for transforming sketches into stylized electronic paintings. A neural network system is trained where the training includes training a first neural network that converts input sketches into output images and training a second neural network that converts images into output paintings. Similarity for the first neural network is evaluated between the output image and a reference image and similarity for the second neural network is evaluated between the output painting, the output image, and a reference painting. The neural network system is modified based on the evaluated similarity. The trained neural network is used to generate an output painting from an input sketch where the output painting maintains features from the input sketch utilizing an extrapolated intermediate image and reflects a designated style from the reference painting.
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