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    Training image-recognition systems using a joint embedding model on online social networks
    33.
    发明授权
    Training image-recognition systems using a joint embedding model on online social networks 有权

    公开(公告)号：US10026021B2

    公开(公告)日：2018-07-17

    申请号：US15277938

    申请日：2016-09-27

    Applicant: Facebook, Inc.

    Inventor： Dirk John Stoop ,  Balmanohar Paluri

    IPC: G06K9/66 ,  G06F17/30 ,  G06K9/72 ,  G06Q50/00 ,  G06K9/62 ,  G06K9/42 ,  G06N99/00

    Abstract: In one embodiment, a method includes identifying a shared visual concept in visual-media items based on shared visual features in images of the visual-media items; extracting, for each of the visual-media items, n-grams from communications associated with the visual-media item; generating, in a d-dimensional space, an embedding for each of the visual-media items at a location based on the visual concepts included in the visual-media item; generating, in the d-dimensional space, an embedding for each of the extracted n-grams at a location based on a frequency of occurrence of the n-gram in the communications associated with the visual-media items; and associating, with the shared visual concept, the extracted n-grams that have embeddings within a threshold area of the embeddings for the identified visual-media items.

    Updating Predictions for a Deep-Learning Model
    34.
    发明申请
    Updating Predictions for a Deep-Learning Model 审中-公开

    公开(公告)号：US20180189672A1

    公开(公告)日：2018-07-05

    申请号：US15394289

    申请日：2016-12-29

    Applicant: Facebook, Inc.

    Inventor： Balmanohar Paluri

    IPC: G06N99/00 ,  G06N7/00

    Abstract: In one embodiment, a system retrieves a first feature vector for an image. The image is inputted into a first deep-learning model, which is a first-version model, and the first feature vector may be output from a processing layer of the first deep-learning model for the image. The first feature vector using a feature-vector conversion model to obtain a second feature vector for the image. The feature-vector conversion model is trained to convert first-version feature vectors to second-version feature vectors. The second feature vector is associated with a second deep-learning model, and the second deep-learning model is a second-version model. The second-version model is an updated version of the first-version model. A plurality of predictions for the image may be generated using the second feature vector and the second deep-learning model.

    Visual CAPTCHA Based On Image Segmentation
    35.
    发明申请
    Visual CAPTCHA Based On Image Segmentation 审中-公开

    公开(公告)号：US20180189471A1

    公开(公告)日：2018-07-05

    申请号：US15395618

    申请日：2016-12-30

    Applicant: Facebook, Inc.

    Inventor： Balmanohar Paluri

    IPC: G06F21/36 ,  G06F21/62

    CPC classification number: G06F21/36 ,  G06F2221/2133

    Abstract: In one embodiment, a method includes receiving a request for a protected resource, providing information to display a challenge-response test, where the challenge-response test includes an image and instructions to provide user input in relation to the image, the image comprises one or more masks, and each of the masks is defined by a perimeter, receiving user input in relation to the image, generating an assessment of the user input based on a correlation between the user input and the masks, determining, based on the assessment, whether the user input corresponds to human-generated input, and if the user input may be deemed responsive to the instructions, then providing information to access the protected resource, else providing information indicating that the user input failed the challenge-response test. Each of the masks may include a classification, and the instructions may provide user input in relation to the classifications.

    Diversifying Media Search Results on Online Social Networks
    36.
    发明申请
    Diversifying Media Search Results on Online Social Networks 审中-公开

    公开(公告)号：US20180101540A1

    公开(公告)日：2018-04-12

    申请号：US15289532

    申请日：2016-10-10

    Applicant: Facebook, Inc.

    Inventor： Dirk John Stoop ,  Balmanohar Paluri

    IPC: G06F17/30

    CPC classification number: G06F16/7867 ,  G06Q50/01

    Abstract: In one embodiment, a method includes receiving a query of a first user; retrieving videos that match the query; determining a filtered set of videos, wherein the filtering includes removing duplicate videos based on the duplicate videos having a digital fingerprint that is within a threshold degree of sameness from that of a modal video; calculating, for each video, similarity-scores that correspond to a degree of similarity between the video and another video in the filtered set; grouping the videos into clusters that include videos with similarity-scores greater than a threshold similarity-score with respect to each other video in the cluster; and sending, to the first user, a search-results interface including search results for the videos that are organized within the interface based on the respective clusters of their corresponding videos.

    METHODS AND SYSTEMS FOR DIFFERENTIATING SYNTHETIC AND NON-SYNTHETIC IMAGES
    39.
    发明申请
    METHODS AND SYSTEMS FOR DIFFERENTIATING SYNTHETIC AND NON-SYNTHETIC IMAGES 有权
    分解合成和非合成图像的方法和系统

    公开(公告)号：US20140241629A1

    公开(公告)日：2014-08-28

    申请号：US13781632

    申请日：2013-02-28

    Applicant: Facebook, Inc.

    Inventor： Apostolos Lerios ,  Dirk Stoop ,  Ryan Mack ,  Lubomir Bourdev ,  Balmanohar Paluri

    IPC: G06T9/00

    CPC classification number: G06K9/4652 ,  G06K9/4604 ,  G06K9/4642 ,  G06K9/6202 ,  G06K9/6267 ,  G06T9/00 ,  H04N19/12 ,  H04N19/136 ,  H04N19/172 ,  H04N19/27 ,  H04N19/40 ,  H04N19/60 ,  H04N19/85 ,  H04N19/86

    Abstract: The techniques introduced here include a system and method for transcoding multimedia content based on the results of content analysis. The determination of specific transcoding parameters, used for transcoding multimedia content, can be performed by utilizing the results of content analysis of the multimedia content. One of the results of the content analysis is the determination of image type of any images included in the multimedia content. The content analysis uses one or more of several techniques, including analyzing content metadata, examining colors of contiguous pixels in the content, using histogram analysis, using compression distortion analysis, analyzing image edges, or examining user provided inputs. Transcoding the multimedia content can include adapting the content to the constraints in delivery and display, processing and storage of user computing devices.

    Abstract translation: 这里介绍的技术包括基于内容分析结果对多媒体内容进行代码转换的系统和方法。 可以通过利用多媒体内容的内容分析结果来执行用于代码转换多媒体内容的特定代码转换参数的确定。 内容分析的结果之一是确定包含在多媒体内容中的任何图像的图像类型。 内容分析使用几种技术中的一种或多种，​​包括分析内容元数据，使用直方图分析，使用压缩失真分析，分析图像边缘或检查用户提供的输入来检查内容中连续像素的颜色。 多媒体内容的转码可以包括使内容适应于用户计算设备的传送和显示，处理和存储中的约束。

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