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    Intelligent online personal assistant with image text localization
    12.
    发明申请
    Intelligent online personal assistant with image text localization 有权

    公开(公告)号：US20210224877A1

    公开(公告)日：2021-07-22

    申请号：US17222251

    申请日：2021-04-05

    Applicant: eBay Inc.

    Inventor： Shuai Zheng ,  Robinson Piramuthu

    IPC: G06Q30/06 ,  G06F16/532 ,  G06N3/04 ,  G06N3/00 ,  G06K9/32 ,  G06F16/583 ,  G06N5/02 ,  G06K9/00 ,  G06K9/52 ,  G06K9/62 ,  G06K9/66 ,  G06N3/08 ,  G06Q20/12 ,  G06Q20/40 ,  G06T7/00

    Abstract: Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual text content and input query image visual text content. Unlike conventional optical character recognition (OCR) based systems, embodiments automatically localize and isolate portions of a candidate product image and an input query image that each contain visual text content, and calculate a visual similarity measure between the respective portions. A trained neural network may be re-trained to more effectively find visual text content by using the localized and isolated visual text content portions as additional ground truths. The visual similarity measure serves as a visual search result score for the candidate product. Any number of images of any number of candidate products may be compared to an input query image to enable text-in-image based product searching without resorting to conventional OCR techniques.

    Correlating image annotations with foreground features
    15.
    发明授权
    Correlating image annotations with foreground features 有权

    公开(公告)号：US10853407B2

    公开(公告)日：2020-12-01

    申请号：US14290754

    申请日：2014-05-29

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Robinson Piramuthu ,  Vicente Ordonez-Roman ,  Vignesh Jagadeesh ,  Wei Di

    IPC: G06F16/58 ,  G06F16/583 ,  G06K9/46 ,  G06F16/53 ,  G06F16/50 ,  G06T7/11 ,  G06T7/10 ,  G06F16/532

    Abstract: A machine may be configured to execute a machine-learning process for identifying and understanding fine properties of various items of various types by using images and associated corresponding annotations, such as titles, captions, tags, keywords, or other textual information applied to these images. By use of a machine-learning process, the machine may perform property identification accurately and without human intervention. These item properties may be used as annotations for other images that have similar features. Accordingly, the machine may answer user-submitted questions, such as “What do rustic items look like?,” and items or images depicting items that are deemed to be rustic can be readily identified, classified, ranked, or any suitable combination thereof.

    Integration of 3D models
    17.
    发明授权
    Integration of 3D models 有权

    公开(公告)号：US10586379B2

    公开(公告)日：2020-03-10

    申请号：US15709997

    申请日：2017-09-20

    Applicant: eBay Inc.

    Inventor： Qiaosong Wang ,  James Thomas Dowdell ,  Jinrong Xie ,  Robinson Piramuthu

    IPC: G06K9/00 ,  G06T15/20 ,  G06T7/90 ,  G06T17/20

    Abstract: In various example embodiments, a system and method for integration of a three-dimensional model is disclosed. In one example embodiment, a method includes receiving a plurality of images, selecting points on the images and triangulating the points to generate a plurality of depth maps, generate a three-dimensional mesh by combining the plurality of depth maps, generating a three-dimensional model of the item by projecting the plurality of images onto the mesh using the points, calibrating colors used in the model using colors diffuse properties of the colors in the images, and providing a user interface allowing a user to select one or more user points on the three-dimensional model and provide additional information associated with the selected user points.

    Hierarchical deep convolutional neural network for image classification
    19.
    发明授权
    Hierarchical deep convolutional neural network for image classification 有权

    公开(公告)号：US10387773B2

    公开(公告)日：2019-08-20

    申请号：US14582059

    申请日：2014-12-23

    Applicant: eBay Inc.

    Inventor： Zhicheng Yan ,  Robinson Piramuthu ,  Vignesh Jagadeesh ,  Wei Di ,  Dennis Decoste

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.

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