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1. 发明申请

US20180260975A1 ILLUMINATION ESTIMATION FROM A SINGLE IMAGE 审中-公开
公开(公告)号：US20180260975A1
公开(公告)日：2018-09-13
申请号：US15457192
申请日：2017-03-13
申请人： ADOBE SYSTEMS INCORPORATED
发明人： KALYAN K. SUNKAVALLI , XIAOHUI SHEN , MEHMET ERSIN YUMER , MARC-ANDRÉ GARDNER , EMILIANO GAMBARETTO
IPC分类号： G06T7/90
CPC分类号： G06T7/00 , G06N3/0454 , G06N3/0472 , G06N3/08 , G06N20/10 , G06T2207/10024 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084
摘要： Methods and systems are provided for using a single image of an indoor scene to estimate illumination of an environment that includes the portion captured in the image. A neural network system may be trained to estimate illumination by generating recovery light masks indicating a probability of each pixel within the larger environment being a light source. Additionally, low-frequency RGB images may be generated that indicating low-frequency information for the environment. The neural network system may be trained using training input images that are extracted from known panoramic images. Once trained, the neural network system infers plausible illumination information from a single image to realistically illumination images and objects being manipulated in graphics applications, such as with image compositing, modeling, and reconstruction.

2. 发明申请

US20190147224A1 NEURAL NETWORK BASED FACE DETECTION AND LANDMARK LOCALIZATION 审中-公开
公开(公告)号：US20190147224A1
公开(公告)日：2019-05-16
申请号：US15815635
申请日：2017-11-16
申请人： ADOBE SYSTEMS INCORPORATED
发明人： HAOXIANG LI , ZHE LIN , JONATHAN BRANDT , XIAOHUI SHEN
IPC分类号： G06K9/00 , G06K9/46 , G06N3/08 , G06T15/04 , G06T15/20 , G06F3/0481
摘要： Approaches are described for determining facial landmarks in images. An input image is provided to at least one trained neural network that determines a face region (e.g., bounding box of a face) of the input image and initial facial landmark locations corresponding to the face region. The initial facial landmark locations are provided to a 3D face mapper that maps the initial facial landmark locations to a 3D face model. A set of facial landmark locations are determined from the 3D face model. The set of facial landmark locations are provided to a landmark location adjuster that adjusts positions of the set of facial landmark locations based on the input image. The input image is presented on a user device using the adjusted set of facial landmark locations.

3. 发明申请

US20180267997A1 LARGE-SCALE IMAGE TAGGING USING IMAGE-TO-TOPIC EMBEDDING 审中-公开
公开(公告)号：US20180267997A1
公开(公告)日：2018-09-20
申请号：US15463769
申请日：2017-03-20
申请人： ADOBE SYSTEMS INCORPORATED
发明人： ZHE LIN , XIAOHUI SHEN , JIANMING ZHANG , HAILIN JIN , YINGWEI LI
IPC分类号： G06F17/30 , G06T11/60 , G06T7/33 , G06K9/66 , G06N3/04 , G06N3/08
CPC分类号： G06F17/30268 , G06F17/30277 , G06F17/30864 , G06K9/00684 , G06K9/66 , G06N3/04 , G06N3/08 , G06T7/33 , G06T11/60 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084
摘要： A framework is provided for associating images with topics utilizing embedding learning. The framework is trained utilizing images, each having multiple visual characteristics and multiple keyword tags associated therewith. Visual features are computed from the visual characteristics utilizing a convolutional neural network and an image feature vector is generated therefrom. The keyword tags are utilized to generate a weighted word vector (or “soft topic feature vector”) for each image by calculating a weighted average of word vector representations that represent the keyword tags associated with the image. The image feature vector and the soft topic feature vector are aligned in a common embedding space and a relevancy score is computed for each of the keyword tags. Once trained, the framework can automatically tag images and a text-based search engine can rank image relevance with respect to queried keywords based upon predicted relevancy scores.

4. 发明申请

US20170236032A1 ACCURATE TAG RELEVANCE PREDICTION FOR IMAGE SEARCH 审中-公开
公开(公告)号：US20170236032A1
公开(公告)日：2017-08-17
申请号：US15043174
申请日：2016-02-12
申请人： ADOBE SYSTEMS INCORPORATED
发明人： ZHE LIN , XIAOHUI SHEN , JONATHAN BRANDT , JIANMING ZHANG , CHEN FANG
IPC分类号： G06K9/62 , G06N3/08 , G06F17/30
CPC分类号： G06K9/623 , G06F16/24578 , G06F16/285 , G06F16/51 , G06F16/583 , G06K9/4628 , G06K9/6223 , G06K9/6262 , G06K9/6276 , G06N3/0454 , G06N3/08 , G06N20/10
摘要： 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.

5. 发明申请

US20180267996A1 TOPIC ASSOCIATION AND TAGGING FOR DENSE IMAGES 审中-公开
公开(公告)号：US20180267996A1
公开(公告)日：2018-09-20
申请号：US15463757
申请日：2017-03-20
申请人： ADOBE SYSTEMS INCORPORATED
发明人： ZHE LIN , XIAOHUI SHEN , JIANMING ZHANG , HAILIN JIN , YINGWEI LI
IPC分类号： G06F17/30 , G06T11/60 , G06T7/33 , G06K9/66 , G06N3/04 , G06N3/08
CPC分类号： G06F16/5866 , G06F16/532 , G06F16/951 , G06K9/00684 , G06K9/4628 , G06K9/4676 , G06K9/6248 , G06K9/6273 , G06K9/66 , G06N3/04 , G06N3/0454 , G06N3/08 , G06T7/33 , G06T11/60 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084
摘要： A framework is provided for associating dense images with topics. The framework is trained utilizing images, each having multiple regions, multiple visual characteristics and multiple keyword tags associated therewith. For each region of each image, visual features are computed from the visual characteristics utilizing a convolutional neural network, and an image feature vector is generated from the visual features. The keyword tags are utilized to generate a weighted word vector for each image by calculating a weighted average of word vector representations representing keyword tags associated with the image. The image feature vector and the weighted word vector are aligned in a common embedding space and a heat map is computed for the image. Once trained, the framework can be utilized to automatically tag images and rank the relevance of images with respect to queried keywords based upon associated heat maps.

6. 发明申请

US20170236055A1 ACCURATE TAG RELEVANCE PREDICTION FOR IMAGE SEARCH 审中-公开
公开(公告)号：US20170236055A1
公开(公告)日：2017-08-17
申请号：US15094633
申请日：2016-04-08
申请人： ADOBE SYSTEMS INCORPORATED
发明人： ZHE LIN , XIAOHUI SHEN , JONATHAN BRANDT , JIANMING ZHANG , CHEN FANG
IPC分类号： G06N3/08 , G06N3/04
CPC分类号： G06N3/08 , G06F17/30247 , G06N3/0454 , G06N3/0472 , G06N99/005
摘要： 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.

7. 发明申请

US20170004383A1 SEARCHING UNTAGGED IMAGES WITH TEXT-BASED QUERIES 审中-公开
标题翻译：使用基于文本的查询搜索未经处理的图像
公开(公告)号：US20170004383A1
公开(公告)日：2017-01-05
申请号：US14788113
申请日：2015-06-30
申请人： ADOBE SYSTEMS INCORPORATED
发明人： ZHE LIN , JONATHAN BRANDT , XIAOHUI SHEN , JAE-PIL HEO , JIANCHAO YANG
IPC分类号： G06K9/62 , G06K9/66 , G06F17/30
CPC分类号： G06F17/30268 , G06F17/30277 , G06K9/6215 , G06T2207/20084
摘要： In various implementations, a personal asset management application is configured to perform operations that facilitate the ability to search multiple images, irrespective of the images having characterizing tags associated therewith or without, based on a simple text-based query. A first search is conducted by processing a text-based query to produce a first set of result images used to further generate a visually-based query based on the first set of result images. A second search is conducted employing the visually-based query that was based on the first set of result images received in accordance with the first search conducted and based on the text-based query. The second search can generate a second set of result images, each having visual similarity to at least one of the images generated for the first set of result images.
摘要翻译：在各种实现中，个人资产管理应用被配置为执行操作，其便于搜索多个图像的能力，而不管基于简单的基于文本的查询，具有与其相关联的或不具有特征标签的图像。通过处理基于文本的查询以产生用于基于第一组结果图像进一步生成基于视觉的查询的第一组结果图像来进行第一搜索。使用基于基于根据所进行的第一次搜索接收的第一组结果图像并基于基于文本的查询的基于视觉的查询进行第二搜索。第二搜索可以产生第二组结果图像，每个结果图像与对于第一组结果图像生成的图像中的至少一个图像具有视觉相似性。

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